Objective: Scaphoid fractures, particularly occult and non-displaced fractures, are difficult to detect using traditional X-ray methods due to their subtle appearance and variability in bone density. The study proposes a two-stage CNN approach to detect and classify scaphoid fractures using anterior-posterior (AP) and lateral (LA) X-ray views for more accurate diagnosis. Methods: The study emphasizes the use of multi-view X-ray images (AP and LA views) to improve fracture detection and classification. The multi-view fusion module helps integrate information from both views to enhance detection accuracy, particularly for occult fractures that may not be visible in a single view. The proposed method includes two stages that are stage 1: Detect the scaphoid bone using Faster R-CNN and Feature Pyramid Network (FPN) for region proposal and small object detection. The detection accuracy for scaphoid localization is 100% with Intersection over Union (IoU) scores of 0.8662 for AP views and 0.8478 for LA views. And stage 2: Perform fracture classification using a ResNet backbone and FPN combined with a multi-view fusion module to combine features from both AP and LA views. This stage achieves a classification accuracy of 89.94%, recall of 87.33%, and precision of 90.36%. Results: The proposed model performs well in both scaphoid bone detection and fracture classification. The multi-view fusion approach significantly improves recall and accuracy in detecting fractures compared to single-view approaches. In scaphoid detection, both AP and LA views achieved 100% detection accuracy. In fracture detection, using multi-view fusion, the accuracy for AP views reached 87.16%, and for LA views, it reached 83.83%. Conclusion: The multi-view fusion model effectively improves the detection of scaphoid fractures, particularly in cases of occult and non-displaced fractures. The model provides a reliable, automated approach to assist clinicians in detecting and diagnosing scaphoid fractures more efficiently.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

Recent legalization and normalization of euthanasia through the government program of medical assistance in dying (MAiD) is impacting Canada's population dynamics and is now the fifth leading cause of death. Ongoing proposals suggest expanding MAiD eligibility to mental illness. A knowledge gap exists to determine to what degree this expansion of MAiD may exacerbate the existing challenges associated with an aging population and declining birth rates. It may also complicate meeting national population targets of 100 million by 2100 set by the Century Initiative. To fill these knowledge gaps, this study conducts a comprehensive evaluation of the prevalence of mental illness in Canada. The results show that if MAiD is expanded to mental disorders under the high MAiD scenario by mid-century it will impact about 12.3% of the population. The low MAiD scenario projects an increase from 45,786 to 227,024 additional deaths/year. The likely scenario shows MAiD growing from 214,499 to approximately 896,312 by mid-century. Thus, if MAiD is expanded to mental illness under the likely scenario, more than half a million additional immigrants will be needed per year. These results emphasize the importance of integrating immigration policies with MAiD practices to ensure demographic stability and achieve long-term population objectives.

Background: The Covid-19 pandemic was in reality a syndemic there the balance between a multitude of biological (Nature-made) and socio-political (men-made) factors determined individually the outcome of every single infection, on a scale of nothing-to-fatal. Blaming the virus for everything bad that happened to us after January 2020 is incorrect. Now, in ~ 2 years distance from the syndemic, it may be timely and possible to uncover the human contribution to this historical event and learn of it. Objective: To identify and use some qualitative approach to estimate the objective, statistical / epidemiological ‘value’ of the virus-selected individuals (in age-normalized groups) for the rest of the society. This non-political, non-religious approach may be the only way to understand the benefit / cost ratio of actions and emergency orders we had to survive in addition to the virus itself. Methods: Statistical data were obtained from publicly available official databases, including the relevant actuarial tables and evaluated by widely used statistical methods. The estimation of statistical/epidemiological ‘value of life’ was based, strictly and exclusively, on the real age and the statistically calculated Residual Life Expectancy (RLE, i.e. remaining years alive at a given age). The American healthcare expenses were compared to the corresponding healthcare costs/capita in other similarly well-developed west-European countries. This was the only available method to get some ideas about the magnitude of the well-established (rational) healthcare spending and compare it to the American standards. Results 1) Life expectancy uninterruptedly increased in USA since 1933, due to continuous reduction of Mortality Rate of the youngest and oldest citizens. COVID didn’t stop the population growth (+8.2 M /2020-2023). 2) The total count of Excess COVID associated Deaths (XCD) is ~ 2-times as much as the official statistic was able to explain by virus infection. It is speculated that these Complementary Covid Deaths (CCD) are probably the unexpected side-effects of the pandemic related emergency orders. 3) The worldwide ‘longevity’ trend exponentially increased the healthcare expenses, especially the care of elderly. However the USA is unique in this aspect and the healthcare costs are irrationally much higher than in any other country. 4) We can see a large economic incentive in the development of excesses under COVID syndemic. Conclusions The harm of the COVID virus - as well as the benefits of the mandatory, emergency actions during the pandemic - were grossly over-estimated. Professional, unbiased (non-religious, non-political) and independent reviews are strongly recommended.

Search for the new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human res-piratory system is electromagnetic sensing, based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the elec-tromagnetic signal passing through the body. Results of experiments on recording changes in the level of electromagnetic radiation passing through the human body, performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated.

The last wave of Artificial Intelligence is dedicating particular emphasis to the line of explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided-diagnosis systems and its usage for knowledge discovery are collecting interest in the Medical Imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI were focused on interpreting the predictions returned by deep neural networks, typically including attribution techniques with saliency map approaches and other feature visualization methods. However, these are often criticized for providing incorrect and incomplete representations of the black-box models’ behaviour. This raises the attention in proposing models intentionally designed to be self-explanatory. In particular, part-prototype (PP) models are interpretable-by-design computer vision (CV) models that base their decision process in learning and identifying representative prototypical parts from the input images, and they are collecting increasing interest and results in MI applications. This narrative review provides a summary of existing PP networks, their application in MI analysis and current challenges.

The objective of this study was to develop and evaluate artificial intelligence (AI) models for the detection and instance segmentation of vertebrae and spinal metastases in computer tomography (CT) scans. The models were trained on datasets consisting of patients with polytrauma and relatively undamaged spines, as well as patients diagnosed with spinal metastases. Our results indicate that the models achieved high performance in vertebra segmentation, with F-beta scores ranging from 0.88 to 0.96 across all vertebra classes. For spinal metastases, the model attained F-beta scores of 0.68 for lytic type and 0.57 for sclerotic type metastases. Additionally, the models demonstrated the capability to detect isolated metastatic nodes in other bones, highlighting their robustness and potential for broader clinical application. Despite these promising results, the study faced limitations including a relatively small and homogeneous dataset, variability in segmentation mask quality, and the need for real-world clinical validation. Future clinical trials are necessary to evaluate the practical utility and effectiveness of these AI models in improving patient outcomes. Our study emphasizes the potential of AI-assisted detection and segmentation models in enhancing diagnostic accuracy and efficiency in clinical practice.

The objective of this research project was to evaluate the effects of the addition of inorganic fillers into PEBA at various loading levels on the processing, mechanical, thermal and physical properties of the polymer. The composites investigated in this study were Pebax® 6333 SA01 MED (PEBA100) with 10 wt.%, 20 wt.% and 30 wt.% Barium Sulphate (PEBA/BaSO4) and Bismuth Oxychloride (PEBA/BiOCl) respectively. Composites were prepared using twin screw extrusion and injection moulding. Post-processing, composite samples were analysed for their mechanical properties (via tensile, impact and flexural analysis), thermal properties (via differential scanning calorimetry), rheological performance (via melt flow index) and physical properties (via density analysis and ash content testing). The results of which reveal how filler type and concentration influence PEBA composites. The results of the study suggest that BiOCl filler enhances the aesthetic properties, increases the stiffness, while maintaining some level of flexibility with minimal adverse effects on the tensile and impact strength. When comparing BiOCl to BaSO4 filled composites, it was clear that depending on the application of the polymer composite, BiOCl may provide more desirable properties. The study highlights the importance of optimising filler concentration and processing conditions to achieve desired composite properties for specific medical applications.

Medical laboratories are perhaps the largest measurement industry in the world. The metrology terminology is relevant for effective and efficient communication, particularly where metrology activities are carried out by operators with different metrology skills. WASPaLM and SIPMeL have had some opportunities to propose changes to the documents in preparation to CLSI and ISO in order to harmonise the terminology with the Metrology Vocabulary (VIM). Many proposals have been accepted. Here we summarise some particularly critical points for metrological terms. The main terms discussed are: Measuring, measuring range, examination, pre-examination, post-examination, manufacturer, measuring instrument, quantitative, qualitative, semi-quantitative, processing, easurement error, maximum permissible error of measurement, total error of measurement, monitoring, variability, performance, reliability, influence, interference, selectivity, sensitivity, detection limit, reliability, pomparability, compatibility, control material. Despite all efforts to coordinate terminologies, it is inevitable that overlapping and inconsistent terminologies will continue to be used because documents and policies are produced in different contexts. In some ISO and CLSI documents, phenomena of magnetic attraction towards common words (such as “analysis” and derivatives), without any consideration of the true metrological meaning, are noted. The ISO and CLSI working groups show, alongside moments of openness, phenomena of true self-referential conservatism.

This investigation pioneers an examination of the scarcely explored terrain of social diversity in medical education, assessing the complex impacts of socioeconomic status (SES), prior working experiences, and completed apprenticeships on a spectrum of psychological and academic facets among first-year medical students (n = 336) in an urban university setting. By utilizing a systematic and detailed approach, the study illuminates prior anatomical knowledge and various psycholog-ical constructs, marking a significant stride into a domain where knowledge remains profoundly limited. Analyses indicated that SES significantly affects financial anxiety (F(4, 331) = 17.391, p < .001) and academic behavioral confidence (F(4, 331) = 4.323, p = 0.002). Students with prior working experience reported higher self-perceived clinical experience but lower A-level grades (t(279.459) = -6.690, p < .001), competence in natural sciences (t(321.045) = -3.178, p = 0.002), and online competence (t(319.429) = -2.026, p = 0.044). Those who completed an apprenticeship showed higher resilience, academic confidence, self-efficacy, and greater concerns about balancing studies and work (t values ranging from 2.020 to 3.158, p < 0.05). Correlation analysis revealed a positive relationship between resilience (RQS) and coping with academic stress (CAS) (r = 0.632, p < .001), as well as between resilience and academic behavioral confidence (ABC) (r = 0.608, p < .001). Negative correlations were observed between resilience, coping with academic stress, academic confidence, and cognitive test anxiety (CTA) (r values from -0.235 to -0.404, p < .001). The findings emphasize the need for tailored support for students from diverse SES backgrounds and with varying expe-riences. The study highlights the value of a comprehensive approach in medical education, con-sidering the diverse backgrounds and experiences of students. Future research should explore the long-term impact of these factors on professional competencies and patient care, leveraging the diversity of the student body for a holistic educational experience.

This study evaluates the preparedness of Siloam Hospital Bali for medical tourism using Actor-Network Theory (ANT) to analyze human and non-human interactions within social networks. The objective is to identify factors influencing the hospital's readiness. Data was collected through interviews with hospital administration, specialized physicians, local officials, and tourism organization members, supplemented by document and archival research. Network visualization with GraphCommons.com revealed interaction patterns, key actors, and potential impediments. The results show that effective hospital administration, professional care by doctors, and cooperation with tourism groups and local authorities significantly impact the hospital's readiness. However, dependencies on core nodes, lack of redundancy, and underutilization of peripheral nodes are vulnerabilities. Recommendations include enhancing integration with peripheral nodes, establishing network redundancy, involving educational institutions, and aligning with international insurance and immigration policies. This study contributes to a comprehensive understanding of medical tourism dynamics and offers strategies to enhance Siloam Hospital's readiness.

Background: The CoBaTrICE initiative standardized intensive care training throughout Europe. Pericardiocentesis, an important yet infrequent operation, poses unique training challenges often unmet by conventional methods. This study evaluates the efficacy of virtual reality (VR) simulations and 3D-printed mannequins in pericardiocentesis training, both crafted from scratch using highly affordable materials and free software. Methods: Thirty-five final-year medical students participated in this prospective, quasi-experimental study. Initially, students underwent VR simulation training, followed by training using 3D-printed self-made mannequins. Learning outcomes were assessed using the Objective and Structured Clinical Examination (OSCE) questionnaire. Heart rate variability (HRV) analysis was employed to monitor stress responses, while the NASA Task Load Index (NASA-TLX) was used to gauge self-perceived difficulty. Results: Although the mannequin model induced more stress reactions, it excelled in tasks requiring fine motor skills, such as correct placement and asepsis maintenance. In contrast, lower NASA-TLX scores indicated that the VR model imposed less mental demand and effort, reflecting a reduced cognitive load. Conclusion: Both VR simulations and 3D-printed mannequins effectively taught pericardiocentesis skills, each offering distinct advantages. Mannequins enhanced fine motor skills, whereas VR reduced cognitive load and increased engagement. The combination of both approaches could maximize training outcomes, particularly in resource-constrained environments, thus broadening access to advanced simulation education.

This study investigated the prevalence and associated risk factors of Lower Back Pain (LBP) among medical students of Alfaisal University, Riyadh, Saudi Arabia. A cross-sectional style was employed where 331 medical students were evaluated using the Oswestry Disability Index (ODI) supplemented with demographic and lifestyle questions. Data analysis allowed identification of LBP-risk factor relationships. The study revealed a 73.4% prevalence of LBP among Alfaisal University medical students with a 4.61 average severity. Female students, those who spent long hours on computers, and those who did not exercise were more likely to experience LBP. Higher disability degree was associated with participants aged &gt;21, used their devices for extended periods, and insufficient sleep. LBP is highly prevalent with several risk factors existing. These findings highlight the need for targeted interventions such as ergonomic/lifestyle improvements and reduced device use.

Recent developments in flexible printed heaters (FPH) for wearable thermal applications, driven by the advancement of printed electronics, show great promise in revolutionizing patient care through the development of wearable flexible heaters for medical applications. Wearable heaters with high thermal stability, heat uniformity, safety, flexibility, comfort, biocompatibility, and power efficiency are desirable for stand-alone medical thermotherapy applications. This paper reviews recent advancements in the design of FPHs for wearable thermal applications. Materials for FPHs, fabrication methods, designs, temperature control mechanisms, medical applications, and performance analysis of specific FPHs are all thoroughly discussed. Materials for FPHs, such as conductive and substrate materials, have received special attention, along with the heater design parameters. Finally, the challenges and future directions for developing FPHs for wearable medical applications are addressed.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all aspects of an organism's functional maintenance and development. All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

Biomaterials have been the subject of extensive research, with their applications in medicine and pharmacy expanding rapidly. Collagen and its derivatives stand out as valuable biomaterials due to their high biocompatibility, biodegradability, and lack of toxicity and immunogenicity. This review comprehensively examines collagen from various sources, its extraction and processing methods, and its structural and functional properties. Preserving the native state of collagen is crucial for maintaining its beneficial characteristics. The challenges associated with chemically modifying collagen to tailor its properties for specific clinical needs are also addressed. The review discusses various collagen-based biomaterials, including solutions, hydrogels, powders, sponges, scaffolds, and thin films. These materials have broad applications in regenerative medicine, tissue engineering, drug delivery, and wound healing. Additionally, the review highlights current research trends related to collagen and its derivatives. These trends may significantly influence future developments, such as using collagen-based bioinks for 3D bioprinting or exploring new collagen nanoparticle preparation methods and drug delivery systems.

Introduction After the first use of infliximab in Crohn’s disease in 1995, the wide scale introduction of anti-tumour necrosis factor alfa (anti-TNF alfa) medications revolutionized the treatment of inflammatory bowel diseases (IBD’s). During the last two decades an increased number of molecules with multiple mechanisms of action were approved for the treatment of IBD with substantial increase of the costs of related to therapy. The increasing costs and access to therapy became a concern of payers and regulators but also for healthcare professionals. Biosimilars are biologic medical products that are highly structurally identical to their reference products and without any clinically meaningful differences in immunogenicity, safety or effectiveness. The introduction of biosimilars was seen as a way to provide a lower-cost alternative to the originator anti-TNF molecules and hence to increase treatment access and availability for patients. Material and methods This an observational, prospective study conducted in two IBD centers in Bucharest. The study included 53 patients, 27 male(M) and 26 female(F), diagnosed with UC or BC according to standard clinical,endoscopic, radiological, histological criteria that were non medically switched to a biosimilar. All patients completed at least the induction treatment with the originator adalimumab and they were clinically stable. After switch they received one of the market available biosimilars of Adalimumab(Hukyndra, Imraldi or Hyrimoz). For each patient, the following data were collected: age, sex, BC or UC diagnosis, previous and current therapy, clinical evolution, endoscopic scores as well as several biological markers. Aim The primary objective was the maintenance of clinical remission at twelve months after switching the original Adalimumab treatment to the biosimilar Adalimumab. The secondary objectives were to evaluate the presence of adverse effects and the treatment persistence at one year. Results In our population, there were 42 patients with Crohn's disease and 11 with ulcerative colitis who switched from the originator adalimumab to a biosimilar Adalimumab. The average age was 44,07 years old for patients with BC and 51,72 years old for patients with UC. No significant differences were found in terms of CF and CRP levels at 6 and 12 months after changing the originator biologic treatment to a biosimilar. Only one patient required changing the biological treatment, following the clinical and biological loss of response. The main adverse effect reported by patients was pain at the injection site. Of the 53 patients, only 2 patients reported pain at the injection site, but this was considered not significant because the patients had a BMI below 20 and they also reported the same symptom when administering the originator. One patient reported experiencing abdominal pain and rectal bleeding immediately after switch, but no recurrence was observed clinically and endoscopically. Conclusion: This observational study is the first carried out in Romania that shows that the after the non-medical switch, biosimilars of Adalimumab were as efficient as the originator Adalimumab in the clinical practice of patients with IBD. These results support that conclusion that the large-scale use of biosimilars does not significantly affect the effectiveness of the therapy and the safety of the patient.

Analytical methodologies were developed based on the near infrared spectroscopy (NIR) associated with chemometric tools to predict the content of macronutrients (total carbohydrate, total protein, and total lipids) in medical food. We prepared the samples employed in this study according to a mixture design with percentual variations from 25% to +25% of macronutrients. Partial least squares (PLS) regression models were built with 70 and 30 samples as calibration and validation sets, respectively. We chose the best processing techniques and results based on the parameters of the ASTM 1655 standard. The models were used to determine these macronutrients in marketed enteral nutrition products, of three different batches, that have quality certification. The average errors in the determination of total carbohydrate, total protein, and total lipids were 1.8, 4.3, and 2.3 %, respectively. The percentages of errors found in this study suggest that the developed model may be useful for the quality control laboratory routine.

Deep convolutional neural networks (CNNs) have revolutionised computer vision technology by automatically learning hierarchical representations of image data, leading to state-of-the-art performance in visual recognition tasks. This article presents a comprehensive review of deep CNNs, from their evolution and architectures to the current state-of-the-art research. The review also provides the core concepts and building blocks of CNNs and their concise mathematical representations. Furthermore, it explores applications of deep CNNs in three popular domains, including medical diagnosis, remote sensing, and facial recognition. This review will benefit researchers and practitioners in computer vision and artificial intelligence, as well as industry professionals seeking to leverage the latest advancements in deep learning technologies.

Emergency Medical Response Systems (EMRS) play a vital role in delivering medical aid during disasters. In Serbia, these systems encounter several obstacles due to economic limitations, scarce resources, and ongoing healthcare reforms, all of which affect their performance. This study focuses on examining the risk factors and effectiveness of Serbia's EMRS, identifying systemic issues, and proposing enhancements to improve emergency preparedness for disasters. A survey was conducted across healthcare institutions in Serbia to gather comprehensive insights. The survey explored various aspects, including organizational structures, resource distribution, communication systems, and the speed of emergency responses. Statistical methods such as Pearson's correlation, multivariate regression analysis and chi-square tests were utilized to interpret data collected from 172 healthcare institutions. The multivariate regression analysis of EMS organization identified that factors such as the structuring of working hours and shift patterns are significant predictors, contributing to small but meaningful variations in various EMS outcomes. In contrast, other factors, such as financial resources and the availability of ambulance vehicles, showed more targeted significance depending on the specific EMS aspect being analyzed. Institutions with written mass casualty plans and procedures demonstrate superior effectiveness in EMS activities, excelling in resource allocation, operational continuity, and emergency preparedness, particularly through enhanced coordination and specialized systems for managing large-scale emergencies. The findings highlighted notable disparities in how EMRS are organized, with only 46% of institutions having specific emergency departments. Resource limitations were evident, as many facilities operated with minimal staff and outdated equipment. The communication systems revealed weaknesses, with just 54% having established protocols for handling emergency calls. Response times frequently surpassed international standards, which negatively affected patient outcomes. Additionally, the study found training deficiencies, with 68% of participants expressing a need for further education. Serbia’s EMRS are significantly impacted by systemic issues such as insufficient funding, disjointed service delivery, and inadequate training. Overcoming these challenges necessitates a comprehensive approach that includes boosting financial support, implementing standardized protocols, and fostering enhanced collaboration across different sectors.

Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.

The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. This study inves-tigates the current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Utilizing a combination of recent technological de-velopments such as AI-driven navigation systems, real-time monitoring, and secure payload management, the study examines how drones can mitigate logistical barriers like traffic conges-tion and geographical isolation. The research methodology includes a comprehensive review of recent case studies from various regions, illustrating the practical applications and benefits of drones in healthcare. The results demonstrate a substantial reduction in transportation time and costs, along with improved accessibility to healthcare services in underserved areas. The study concludes that, while challenges such as regulatory hurdles and privacy concerns remain, the on-going advancements in drone technology and supportive regulatory frameworks have the poten-tial to revolutionize medical logistics, ultimately improving patient outcomes and healthcare de-livery.

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.

Background: An outbreak of Mpox disease caused by a mutant clade 1b virus (MPXV) in Kamituga area, South Kivu Province, DRC has rapidly grown to involve neighboring countries in this region. This now has the authorities worried, due to the limited supply of Mpox vaccines with emergence use licensure (EUL) available. As an emergency local response to this challenge, we set out to identify epitopes of clade 1b MPXV surface proteins as targets for the rapid research and development (R & D) of Mpox medical countermeasures. Methods: The H3L (OPG108–heparin binding protein) and A17L (OPG143–myristylated protein) were selected by virtue of their surface localization, stability and known role in MPXV host cell fusion entry. Within the immune epitope database analysis resource (IEDB-AR), surface accessibility, antigenicity and 3-D localisation (DiscoTope) we examined to identify > 9-nomer epitopes Results: Three (3) and six(6) candidate >9-nomer epitopes were derived for H3L and A17L surface fusion protein. Across all parameters, the two best identified epitopes were as follows:—H3L (28-HINDQKFDDVKDNEVMQEKR-47 and 236-YVEHDPRLVAEH-247) and A17L (104-QNEVTPEYIKDLKYATDYIA-123;) 270-KKDYLLGDSDSVAKCCSKTNTKHCPKIFNNNYKTEHC-290). Conclusions: These epitopes— alone, multiplexed (say as a nano-spiked virus-like particles, VLPs) or fused as a chimeric recombinants form immunogens for the rapid production of protein sub-unit vaccines. Inversely, derivative monoclonal antibodies (mAbs) from the same are precursors for therapeutic cocktails and or antigen, rapid diagnostic tests (RDT).

This paper reviews the ethical issues involved and argues that each of the usual policies for handling conscientious refusals faces serious challenges. Healthcare providers who refuse to provide medical services should be expected to explain their reasons, make prompt referrals, and bear some of the resulting costs or burdens.

Background: The integration of unmanned aerial vehicles (UAVs), commonly known as drones, into medical logistics offers transformative potential for the transpor-tation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. Methods: This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation under various weather conditions. Blood samples, including EDTA whole blood, Serum, Li-Heparin Plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing-Bablok regression and Bland-Altman plots. Results: The results demonstrated that drone transportation maintains blood sample integrity comparably to traditional car transpor-tation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, Lithium-Heparin, and Citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges.

Background: Amidst escalating climate change concerns, sustainable practices in healthcare logistics are critical. This study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across diverse terrains and weather in Liechtenstein and Switzerland. Methods: We conducted a comparative analysis of CO2 emissions, transport distances, and delivery times for each transportation modality. Data collection involved multiple runs under various conditions, with CO2 emissions measured using onboard systems, and transport efficiency assessed via GPS tracking and time monitoring. Results: Combustion cars exhibited the highest average CO2 emissions at 159.5 grams per kilometer (g/km). Electric cars emitted significantly less, averaging 3.43 g/km, which is 2.15% of combustion car emissions. Drones were the most environmentally friendly, with an average emission of 0.09 g/km, representing 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also showed substantial transport efficiency, covering 17% shorter distances on flat terrain and 24% shorter in mountainous areas, with time savings ranging from 13% to 80%. Conclusion: This study highlights the potential of drone technology to revolutionize healthcare logistics by reducing environmental impact and optimizing operational efficiency. Integrating drones can significantly lower the carbon footprint, improve sample integrity, and enhance patient care, promoting a greener, more resilient healthcare system.

The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

Background: Vaccine hesitancy, identified by the World Health Organization as a top ten global health threat, presents a significant barrier to public health efforts, exacerbated by the politicization of vaccines during the COVID-19 pandemic.1 Motivational interviewing (MI) has been shown to be effective in addressing difficult topics, including vaccine hesitancy, emphasizing the need for its integration into medical education.2 Objective: This study explores ways to enhance the medical student curriculum on MI, specifically targeting the management of conversations with vaccine-hesitant patients. These authors seek to evaluate the impact of different teaching approaches on the efficacy of MI training within clinical education. Methods: A blinded study design was implemented, comparing two groups of medical students trained using distinct MI teaching methods: Learning Through Experience (LTE) and Traditional Teaching (TT). The LTE group engaged with a Vaccine Hesitant Standardized Patient (VHSP) before and after an instructional module on the use of MI to combat vaccine hesitancy. In contrast, the TT group interacted with a control Standardized Patient (SP) before the same module and then with the VHSP. Any medical student above the age of 18 was eligible for this study. All students in a vaccine elective at RVUCOM on the Colorado and Utah campuses were enrolled, as well as student volunteers. We excluded any students who opted out of the study. The effectiveness of each method was assessed through confidence and performance scores derived from participant self-evaluations and standardized patient feedback, respectively. Results: Participants in the TT group demonstrated superior mastery of MI skills as taught in the didactic session based on statistically significant findings across more tested variables. The LTE group exhibited enhanced ability in practical applications with standardized patients based on statistically significant findings. Both groups showed increased confidence in MI techniques, with the LTE group reporting greater confidence in vaccine knowledge and MI abilities. That said, the LTE method did not drastically outperform the TT approach in overall efficacy. Conclusions: The study indicates that while traditional TT training effectively imparts MI skills, the LTE approach may offer benefits in applying these skills in clinical scenarios. These findings suggest the potential for integrating multiple teaching methods into the medical curriculum to better prepare students for engaging with vaccine-hesitant patients. Further research is needed to explore the long-term effects of these teaching strategies on student performance and patient outcomes.

The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios. Despite its advancements, SAM encountered limitations in handling some complex low-level segmentation tasks like camouflaged object and medical imaging. In response, in 2023, we introduced SAM-Adapter, which demonstrated improved performance on these challenging tasks. Now, with the release of Segment Anything 2 (SAM2)—a successor with enhanced architecture and a larger training corpus—we reassess these challenges. This paper introduces SAM2-Adapter, the first adapter designed to overcome the persistent limitations observed in SAM2 and achieve new state-of-the-art (SOTA) results in specific downstream tasks including medical image segmentation, camouflaged (concealed) object detection, and shadow detection. SAM2-Adapter builds on the SAM-Adapter's strengths, offering enhanced generalizability and composability for diverse applications. We present extensive experimental results demonstrating SAM2-Adapter's effectiveness. We show the potential and encourage the research community to leverage the SAM2 model with our SAM2-Adapter for achieving superior segmentation outcomes. Code, pre-trained models, and data processing protocols are available at http://tianrun-chen.github.io/SAM-Adaptor/.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for Multiple Sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for Multiple Sclerosis patients.

Additive Manufacturing (AM), commonly known as 3D printing, has revolutionized various industries, including the medical field. This technology enables the creation of complex structures layer by layer, offering unprecedented customization and precision. In medical applications, AM has proven transformative in producing patient-specific implants, prosthetics, and anatomical models, facilitating better surgical planning and outcomes. Additionally, it has shown promise in bioprinting, where cells and biomaterials are used to create tissue-like structures for regenerative medicine. The ability to rapidly prototype and manufacture medical devices reduces time and costs, and enhances personalized patient care. This abstract explores the diverse applications, benefits, and challenges of AM in the medical sector, highlighting its potential to revolutionize healthcare and improve patient outcomes.

The objective of the study was to investigate the dynamics of collaborative learning in TBL through students’ reflections and feedback. The study utilized a mixed-methods approach. Quantitative data collection was done via feedback structured questionnaire. Qualitative data were collected from reflections submitted by 107 first-year MBBS students at the end of TBL session. We found five main themes related to students’ engagement with the session i.e. ( 1) conceptual clarity as the foundation, (2) collaborative learning culture, (3) communication skills, (4) informative and useful team discussions and (5) interactive learning for team development. The majority of the respondents (93%) listened to classmates and coordinated with group members in discussion, (77%) were willing to learn from other group members and contributed ideas to them, (79%) came prepared for session, (75%) found learning material assigned to them as appropriate. Respondents had positive perceptions regarding their participation in group work with a strong inclination towards agreement with the statements provided. The study highlights the positive effects of TBL in promoting peer to peer engagement, active engagement, teamwork skills, and knowledge sharing among undergraduate medical students, ultimately contributing to an effective learning experience.

3D Printing /Additive manufacturing holds a promising future in healthcare as it aids in visualizing things and making 3D objects which is very important in different areas of medicine. The concept of one size fits all has failed the quality of care delivered by us in healthcare which demands customised needs for patient care to create 3D printed tissues, make personalized prosthetics, implants and physical models of the human body. The materials used for printing models and other healthcare devices for medical education, patient needs, and prototyping are increasing. The application of different materials for use in healthcare is not studied. New materials are also available for the various applications in healthcare industry. This experimental study was conducted to review the nine distinct 3D printing filament materials like PETG, PA, ABS, PLA, HIPS, PMMA, Ceramic, Carbon Fiber, and (PLA + Metal Composite) with the help of a 3D printed physical model of T-12 thoracic vertebrae using all the abovementioned materials. The evaluation of materials was based on diverse mechanical properties, applications across medical fields, performance metrics, costs, accuracy, and quality. The assessment This study also focuses on comparison of mechanical properties such as tensile strength, compressive strength of the abovementioned materials with human bone. The study also reviews the 3D printing process and explores its utilization in biomedical applications. The Insights about the application of various materials holds a promising future for development of newer materials for healthcare industry.

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biologi-cal tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross-section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in 2D functional images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) elec-trodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the con-ventional star arrangement. Prior research demonstrated cooperative sensors for measuring bi-oimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to re-move batteries from the sensors and have the central unit supply power over the two-wire bus. Building off our previous research, this paper presents this method applied to the measurements of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application specific integrated circuit (ASIC). Initial experimental results reveal that both approaches are feasible, but the ASIC approach offers ad-vantages for medical safety, as well as lower power consumption and smaller size.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of the portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2,000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2,000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. Follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad application in preventative health care.

This study introduces a data obfuscation technique, leveraging the exponential map associated with the generators of Lie groups. Originating from quantum machine learning frameworks, our method illustrates the practical application of quantum mechanics principles in data processing. Specifically, it employs the exponential map of a generator algebra to introduce controlled noise into the data, achieving obfuscated data while preserving its utility for machine learning tasks. This strategy is shown to safeguard privacy in sensitive datasets, such as discussed medical records, and to enhance dataset volume and diversity through augmentation. Our empirical analysis, benchmarked against standard machine learning approaches, demonstrates that our method can maintain or even improve the predictive accuracy of the original data. This research highlights the potential of Lie group theory for advancing data privacy in medicine, marking a significant contribution to machine learning methodologies by offering the dual benefits of data obfuscation and enrichment. Through this synthesis of algebraic structures and machine learning, we propose new pathways for the secure and effective use of data in sensitive areas.

Este estudio examina la implementación de redes bayesianas para el diagnóstico de afecciones respiratorias utilizando datos clínicos. Se analizaron 5725 registros de una clínica privada en Francia, obtenidos de Kaggle, centrándose en seis variables clave: glucosa en sangre, frecuencia cardíaca, saturación de oxígeno, presión arterial, temperatura y estado de salud del paciente. El objetivo era crear un modelo que mejorara la precisión y eficiencia en el diagnóstico de infecciones respiratorias. Se desarrolló una red bayesiana para modelar las interacciones fisiológicas en condiciones respiratorias, destacando la influencia de la glucosa en sangre en la oxigenación y la interacción entre la frecuencia cardíaca y la saturación de oxígeno. La mediana de la temperatura corporal fue de 38,18 °C y la mediana de la frecuencia cardíaca fue de 92,00 lpm. El modelo, basado en sólidos principios médicos, considera tanto los mecanismos patológicos directos como las respuestas compensatorias del cuerpo. Se utilizó Netica para la simulación y análisis del modelo, proporcionando una visualización clara de las probabilidades condicionales y las relaciones entre las variables. Los hallazgos demuestran que las redes bayesianas son herramientas efectivas para apoyar el diagnóstico de afecciones respiratorias, proporcionando una evaluación integral del estado de salud del paciente. La matriz de confusión reveló una precisión del 94%, una especificidad del 100% y una sensibilidad del 86,53%. Además, la calidad de la prueba para el estado "enfermo" mostró una sensibilidad de hasta el 98,49% y una especificidad de hasta el 98,85%, con un área bajo la curva ROC (AUC) de 0,9592. Estos resultados indican que el modelo tiene un alto rendimiento en la clasificación correcta de los casos de salud y enfermedad normales. Este enfoque tiene el potencial de mejorar la precisión y la eficiencia del diagnóstico, especialmente en entornos clínicos que requieren decisiones rápidas y precisas. Este estudio destaca la aplicación innovadora de las redes bayesianas en medicina respiratoria, ofreciendo una herramienta prometedora para mejorar el diagnóstico y el manejo de pacientes con afecciones respiratorias, y estableciendo una base para futuras investigaciones en el uso de métodos probabilísticos en el diagnóstico médico.

Understanding whether the long-term follow-up (LTFU) system is working for families is critical to measuring the success of newborn screening (NBS) and understanding why some families are lost to follow-up. Caregivers were recruited from six pediatric specialty care clinics. Data was gathered from caregivers via six focus groups (n=24) and open-ended responses on a survey (n=33). Caregiver participants represented a wide range of NBS-related conditions and children’s ages. While this is not the first study to gather caregivers’ input, it is unique in its breadth of perspectives (e.g., metabolic, endocrine, hemoglobinopathy, etc.) and its focus on LTFU. When asked about goals for their children, caregivers identified health-related goals (i.e., ability for children to care for themselves, not being hindered by diagnosis) and non-health related goals (i.e., defining themselves outside of disease, participating in sports, making friends). In qualifying the LTFU care they want and need for their child and the key factors that influence access and engagement, caregivers identified three themes: communication and relationships with providers; care team roles and factors; and care access and utilization factors. The themes identified are not disjoint; they are intertwined and collectively illustrate the lived experiences of families at the epicenter of LTFU care.

Despite soccer popularity, comparatively little scientific information is available concerning the assessment of youth players’ physiological characteristics. The use of medical software integrating wearable sensors to provide accurate and objective data is necessary. In the present study, the objective assessment of physical performance of 128 adolescent soccer players at two timepoints (pre-season and end-season) has been performed. Participants completed a comprehensive test battery including stability, countermovement jumps (CMJ), plyometric jumps (PJ), agility, and quick feet tests. A single Inertial Measurement Unit (IMU) sensor provided quantitative data on various performance metrics. Principal Component Analysis (PCA) reduced the dataset to four principal components (PCs) explaining over 80% of the variance: power (PC1), balance (PC2), speed/agility (PC3), and stiffness (PC4). Paired samples t-test between the two timepoints showed no significant differences in the PCs between the two timepoints, indicating that the training program did not significantly alter the assessed physical attributes. However, subtle individual changes may still inform personalized training adjustments. The study highlights the importance of continuous monitoring and individualized training strategies to optimize athlete performance and reduce injury risk.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

This multi-center retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8% – 97.5%] and a specificity of 97.3% [95% CI: 96.2% - 98.1%]. The application classified positive cases by AD types with an accuracy of 99.5% [95% CI: 98.9% – 99.8%] for type A and 97.5 [95% CI: 96.4% – 98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Accurate segmentation of skin lesions from dermoscopic images is crucial for early skin cancer detection, yet variations in lesion appearance and image artifacts present challenges. This study proposes two deep learning models, UCM-NetV2 and BNN-UCM-NetV2, to improve accuracy and computational efficiency. UCM-NetV2 enhances the UCM-Net architecture with a novel "cyber-structure" combining Multilayer Perceptron and CNN layers, improving prediction accu-racy without increasing the model's parameter count. BNN-UCM-NetV2 further optimizes for mobile deployment by binarizing activations and weights into 1-bit values, reducing computa-tional complexity and model size. Evaluations on the ISIC2017 and ISIC2018 datasets demonstrate that UCM-NetV2 outperforms existing methods in both accuracy and computational efficiency, achieving this with less than 0.04 GFLOPs. These models have the potential to make skin lesion analysis accessible to a broader population, particularly in resource-limited settings, allowing for proactive skin health monitoring and facilitating teledermatology. To foster further innovation in mobile health diagnostics, the source code for UCM-NetV2 will be available on GitHub: https://github.com/chunyuyuan/UCMV2-Net.

Art therapy fosters emotional healing and growth. This process can offer healthcare professionals (HCPs) novel insights into patients’ medication experiences. We developed a Metaverse Art Gal-lery of Image Chronicles (MAGIC), which depicted patients’ medication experiences symbolically as hero-villain portrayals. This gallery aimed to enhance healthcare students’ learning through re-latable insights into patients’ medication therapies. A character sheet was used to craft patients’ personifications of their medication experiences. ChatGPT, NightCafe, Canva, HeyGen and Cam-tasia were used to generate hero-villain portraits based on the character traits and mounted in MAGIC, which consisted of three virtual realms, each with a unique theme. Alpha-testing among sixteen Generation Z healthcare learners indicated that the content in MAGIC enabled them to understand the concepts of medication adherence (93.7%), art therapy (87.5%) and how patients related to their medications (81.3%). Perceived playfulness (rs=0.925, p

Regulation (EU) 2017/745, addresses medical devices and clarifies the scope regarding products containing viable biological materials or organisms. This regulation excludes products containing viable cells or tissues of human or animal origin, as well as those containing viable biological materials or organisms of other origins intended to achieve or support the intended purpose of the products. The term "inviable" is defined as incapable of metabolism or multiplication. The regulation mandates that products manufactured using non-viable biological substances must ensure the highest safety standards, including validated methods of elimination or inactivation of transmissible agents during manufacturing. Several Directives and Regulations provide context for the term "viable" in medical devices, emphasizing the device's ability to perform effectively and safely. Notified Bodies in the EU have interpreted that thermally inactivated microorganisms in a product do not classify it as a medical device under Regulation 2017/745. To address this, it is crucial to differentiate between the inactivation of microorganisms and the viability of their metabolites. Demonstrating the inviability of microorganisms through rigorous testing and evidence is essential for regulatory compliance and safe use in medical products. Clarifying these points and providing necessary evidence can establish that inactivated microorganisms are inviable and meet the requirements for use in medical products.

Mistreatment can adversely affect students’ academic performance and mental health. This study aimed to investigate the prevalence of mistreatment and its association with academic motivation among Thai medical students in clinical years. A cross-sectional study was conducted among medical students throughout Thailand using the Composite Questionnaire of Mistreatment and Academic Motivation Scale (AMS). Descriptive statistics, chi-square tests, correlations, and regression analyses were conducted. A p-value &lt; 0.05 was considered statistically significant. The sample included 399 students, with mean age of 23 ± 1.29 years old, and 61.5% were female. A total of 81% of participants experienced mistreatment. Twenty-nine percents of students thought that mistreatment occurred because the maltreaters did not consider mistreatment as inappropriate or serious. Students who experienced mistreatment exhibited lower AMS (B = -9.16, 95% CI: -15.37 to -2.95, p-value = 0.004) than those who did not. Moreover, students who experienced academic mistreatment exhibited a higher amotivation score (B = 1.50, 95% CI: 0.16 to 2.85, p-value = 0.029) than those who did not. The prevalence of mistreatment was unexpectedly high, and it associated with students' motivation to study. Educational institutions should prioritize addressing this issue and developing strategies to provide assistance, including the enhancement of reporting systems.

With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.

In the context of modern healthcare, the concept of patient-centered care (PCC) has emerged as a crucial aspect, prompting the need for innovative frameworks to empower patients and enhance healthcare outcomes. This paper introduces the Patient-Oriented Medical Ontology Conceptual Framework (POMOCF), a powerful framework aimed at giving patients control over their personal and healthcare information. It prioritizes patient independence by allowing for direct control of electronic health records (EHRs) and facilitates the gathering of information through document upload or scanning. In the POMOCF front-end framework, we propose to maintain scalability, along with robust authorization, authentication, and secure data protocols, all of which are detailed within the framework. This approach enhances data accuracy and security, improving data management efficiency. As part of our ongoing development, we are planning to implement an innovative inference system that will automatically analyze and extract relevant details from the uploaded authorized document, provide real-time updates, and maintain data accuracy. This approach further strengthens data accuracy and prevents unauthorized modifications, thereby making the data management process more efficient. POMOCF leverages medical ontology principles to ensure standardized clinical concepts and improved semantic interoperability. Our integration plans involve integrating genotypic data for personalized medicine initiatives and strengthening the front-end infrastructure. By linking front-end interfaces with advanced technological solutions, we aim to provide effective healthcare delivery, promoting personalized care and enhancing patient outcomes.

- Early and accurate detection of pneumonia from chest X-ray images is crucial for timely treatment and patient care. In study presents a robust computational framework leveraging the CheXnet algorithm, a deep learning model, to identify pneumonia from chest X-rays. Developed a neural network architecture trained on a substantial dataset of chest X- ray images labeled as 'Normal' and 'Pneumonia'. The user- friendly interface of the system allows for seamless uploading of images, with the model subsequently providing predictions, a confusion matrix, accuracy metrics, and layer activations visualizations. The algorithm's performance was rigorously evaluated, with a confusion matrix indicating a high number of true positives and true negatives. Specifically, the model detected pneumonia with an accuracy of 92.47%, as shown by the results displayed in the system's interface. The precision, recall, and F1-score for pneumonia detection were 0.90, 0.96, and 0.93 respectively, with the overall system achieving an accuracy of 0.91 across the test set of 624 images. Visualizations of the neural network layers reveal the complex feature mappings the model uses to differentiate between normal and pathological findings. These insights could be vital for enhancing the interpretability of deep learning models in medical diagnostics. The Results indicate that the CheXnet-based neural network is a promising tool for automating pneumonia detection in clinical settings, which can assist radiologists in the diagnostic process and potentially improve patient outcomes through earlier intervention.

Visualization of volumetric data holds great importance, particularly in medical and biomedical applications. Various imaging techniques, such as FMRI and 3D microscopy, are employed to generate volumetric data, used in medical practice, research, and teaching. Commonly utilized tools like 3D Slicer, Fiji, and MATLAB ® aid in rendering and analyzing 3D images. However, these tools may lack comprehensive rendering functionality and face challenges in handling computational demands as data sizes grow. To address these limitations, this work introduces a GPU-supported renderer with a MATLAB ® interface. This solution gives the user flexible control over rendering parameters and optimizes data transfer through a memory management system. By leveraging the computational power of NVIDIA GPUs, the renderer enables complex and high-quality renderings, enhancing speed and efficiency. It therefore facilitates the analysis and visualization of volumetric data within an integrated environment, namely MATLAB ®, streamlining their workflows. This advancement provides valuable opportunities for researchers and medical professionals to explore and comprehend volumetric data effectively.

Background & Objective: Acne Vulgaris is a common skin disease, with 5 % prevalence in Pakistan and has a significant physiological, psychological, emotional and social impact on the population, our study aims to estimate its annual economic burden per person in Pakistan.Methodology: Economic burden was estimated by using Cost of Illness analysis. All types of costs, including direct medication costs, direct non-medical costs and indirect costs due to loss of work. Data were collected from online databases and literature reviews. Results: Cost of illness analysis estimated that the cost of Acne Vulgaris treatment varies for mild, Moderate and severe acne. Acne is causing an annual economic burden of PKR 1,92,366 PKR per person (direct cost is PKR 1,86,366 and indirect cost 6,000) for an expensive treatment and PKR 53,550 (direct cost is PKR 47550 and indirect cost 6,000) for a cheap treatment. Limitation: Being an LMIC, Pakistan needs extensive study on the pharmacoeconomic analysis of Acne for proper and rational allocation of resources. Conclusion: Acne has a significant economic burden in Pakistan. Even with significant impact of Acne, there is very minute work done about Acne in Pakistan.

Robotic systems are increasingly being used in healthcare to improve patient care and support healthcare professionals. These robots are especially effective at performing repetitive tasks. In complex cancer cases, robots can enhance surgical precision and efficiency, resulting in reduced pain for patients and a lower risk of complications. Robot-assisted surgery offers high-quality 3D imaging, improved clarity, and increased efficiency. This review paper examines the application of Artificial Intelligence (AI) and robotic systems in the healthcare domain, specifically focusing on the interaction among operators, patients, and robots. The goal is to promote collaboration and improve healthcare provision through human-robot interaction. We considered already published articles by relevance, to identify the primary safety issues that need addressing for the integration of AI and robots in hospitals. Concluding with insights into understanding the focal points of social errors in Human-Robot Interaction (HRI) is crucial for mitigating these errors. While current technologies have reached a certain level of maturity, there are still numerous challenges in utilizing robot-assisted clinical practice, including technological, safety, clinical, financial, insurance, psychological, social, ethical, and legal issues. In addition, there is an urgent issue that needs to be addressed: the alignment of values in artificial intelligence, ensuring that AI systems reflect ethical principles and human values is crucial to avoid negative consequences such as bias, discrimination, and prejudicial decisions. Resolving this complex issue requires collaboration among clinical staff, researchers, programmers, and political decision-makers from various fields.

Background: Upper respiratory tract infections (URTIs) are a major public health concern due to their high incidence and potential for complications. Pharmacists often serve as the first point of contact for patients with URTIs. This study aims to develop and evaluate the RESP-8 tool to assist pharmacists in determining the need for medical evaluation. Methods: This prospective interventional cohort study was conducted over six months (October 2023 to March 2024) in community pharmacies. Participants included adults aged 18 years and older presenting with URTI symptoms. Pharmacists used the RESP-8 tool, which comprises eight criteria to assess the need for medical referral. Data on medical history, symptom duration, and progression were recorded. The primary outcome was the feasibility of implementing the RESP-8 tool. Secondary outcomes included the accuracy of the tool, the percentage of patients recommended for medical evaluation, and compliance with these recommendations. Results: A total of 64 participants were included, with an average age of 55.7 years. Pharmacists recommended medical evaluation for 75% (48/64) of patients using the RESP-8 tool. Among these, 70.83% (34/48) complied with the recommendation. The tool demonstrated high accuracy in identifying patients needing further medical evaluation, with only two noted errors. All compliant patients received appropriate treatment without complications. The tool was easy to implement, taking less than 5 minutes per patient. Discussion: The RESP-8 tool effectively guided pharmacists in recommending medical evaluations for URTI patients. The high compliance rate indicates patient trust in pharmacists' guidance. While the study's small sample size limits generalizability, the results are promising. Future studies with larger populations are necessary to validate these findings and further refine the tool. Conclusion: The RESP-8 tool shows potential in enhancing pharmacists' ability to make informed decisions about medical referrals for URTIs, ultimately improving patient outcomes and optimizing healthcare resource management.

Currently, health tourists primarily rely on independent facilitators to manage and book their medical appointments and vacation plans. There is a notable absence of dedicated booking applications for health tourism packages. This paper proposes HealthTourismHub, an application designed to provide personalized packages that include medical appointments, accommodation options and recommended tourism activities. It also serves as a platform for medical experts and accommodation providers, allowing health tourists to discover and connect with them. To incorporate personalization, HealthTourismHub uses an ontology that organizes medical and tourism data, along with a reasoner that generates new knowledge. This approach enables the application to offer customized packages and identify the most suitable providers for each user. Providers are strategically paired and located in close proximity, with the package also including personalized tourism recommendations. A survey was conducted to assess the usability of the application and general perspectives towards health tourism, including motivations, concerns, and preferences. The results revealed an above-average SUS score, indicating that users found the application user-friendly and effective. Some areas for improvement were identified, such as error handling and additional functionalities. Nonetheless, HealthTourismHub shows great potential as a pioneer in the field of health tourism applications.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

The term “medical error” is quite often used to refer to any mistake committed in the provision of healthcare services which has caused harm to a patient. Objective: To analyze civil lawsuits involving medical errors in pediatrics by evaluating: 1) causal link; 2) type of medical error; 3) conviction of the court and its legal basis; 4) correlation between medical expert understanding and legal understanding. Materials and Method: Retrospective analysis of 60 pediatric forensic medical examinations carried out by a pediatric expert between April 2009 and February 2014 at the Institute of Medicine and Criminology of the State of São Paulo (IMESC). The expert’s reports were analyzed according to age, gender, type of forensic medical report (direct or indirect), conclusion of the expert’s report (causal link between the medical conduct and complaint or not), type of medical error (negligence, recklessness, malpractice), judgment in the first instance (if in accordance with the expert’s report or not), and basis of judgment as objective or subjective (type of law). Results: Most expert’s examinations (65%) were indirect (death). In 27 reports (45%), the expert’s conclusion determined that there was a causal link between medical care and the damage inflicted to a patient. Of the 27 reports with causal link, the judge was not in accordance with the expert’s report in one case, thus not considering medical error. The vast majority of sentences (55) were in accordance with the expert’s conclusion (91.6%). Subjective law was mostly used to support the judge’s ruling (61.70%), followed by objective law (23.30%). All lawsuits claimed moral damages (100%). In the statistical analysis of distribution of nominal variables (frequency), an excellent agreement (kappa= 0.86), with statistical significance, was verified between the expert’s report and the court ruling. Conclusion: Expert evidence supported 91.6% of the sentences and was considered to be highly valuable evidence. Expert evidence had a major impact on the legal proceedings and has proven statistically significant (p<0.005). Subjective law was used to assess medical error in all cases in which healthcare professionals were involved. Claims for moral damages was requested in all lawsuits, and was fully or partially granted in 100% of the cases.

(1) Background: Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information, becoming a valuable tool for medicine, particularly for the operating room and surgical learning; (2) Methods: A systematic review was conducted following the PRISMA guidelines to answer the research questions established using the PICO framework; (3) Results: Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes; (4) Conclusion: Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The possibility of integration of distinct medical imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses need consideration. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

Abstract: Circumcision is a long-standing and frequently performed surgical procedure which holds significant cultural significance among AmaXhosa people in South Africa. Due to cultural reasons in some parts of Africa, the integration of medical male circumcision with traditional manhood initiation rituals still lacks acceptance. This study examined the level of knowledge and acceptance of voluntary male medical circumcision (VMMC) among young males in a selected high school in the Nyandeni District of the Eastern Cape Province, South Africa. A descriptive, quantitative, cross-sectional study was conducted, and a self-administered questionnaire was used to obtain information on sociodemographic characteristics, knowledge of VMMC, percep-tions of VMMC, and circumcision practices. One Hundred participants were recruited from both grades 11 and 12, and 82% of the participants indicated that they had received information about VMMC. Most of the respondents (88%) preferred traditional male circumcision (TMC) and only 12% of respondents preferred VMMC. The participants displayed a good understanding of the distinction between VMMC and TMC. However, results from the study showed that most re-spondents exhibited low acceptability towards VMMC. These findings highlight the need to develop evidence-based strategies to enhance learners’ knowledge and acceptance of VMMC.

Algorithmic systems used in healthcare contexts are primarily developed for the benefit of the public. It is therefore essential that these systems are trusted by the individuals for whose benefit they are deployed. Drawing inspiration from the principles embedded in the testing of the safety, efficacy and effectiveness of new medicinal products, concurrent design engineering and pro-fessional certification requirements, the authors propose, for the first time, a preliminary com-petency-based ‘Algorithmic Ethics’ Effectiveness Impact Assessment’ framework for developers of AI systems used in healthcare contexts. They concluded that this set of principles should en-compass the algorithmic systems ‘production lifecycle’, to guarantee the optimized use of the AI technologies, avoiding biases and discrimination while ensuring the best possible outcomes, simultaneously increasing decision-making capacity and the accuracy of the results. As AI is as good as those who program it and the system in which it operates, the robustness and trustworthiness of their ‘creators’ and ‘deployers’, should be fostered by a certification system guaranteeing the latter’s knowledge and understanding of ethical aspects as well as their com-petencies in integrating these aspects in trustworthy AI systems when used in healthcare con-texts.

Background/Objectives Bowel endometriosis (BE) is defined as the presence of endometrial-like tissue in the muscular layer of the bowel wall. When BE does not cause severe obstruction to fecal transit, not leading to (sub)occlusive symptoms, the decision to operate becomes challenging as intestinal surgery is associated with higher complication rates and long-term bowel dysfunction. The aim of this cross-sectional study is to evaluate the quality of life (QoL) in patients with BE undergoing medical therapy or expectant management, investigating the impact of characteristics of BE nodules on QoL and intestinal function. Methods A retrospective cross-sectional analysis was conducted on 580 patients with BE who did not undergo surgery but were treated conservatively by medical therapy or expectant management between January 2017 and August 2022. The diagnosis of BE was done by transvaginal ultrasound and confirmed at double contrast barium enema. After at least one year of follow-up, QoL and intestinal function of patients were evaluated by the Endometriosis Health Profile-5 (EHP-5) questionnaire and Bowel Endometriosis Symptom score (BENS), whereas pain symptoms were assessed by the Visual Analogic Scale (VAS 0-10). Statistical analysis was performed to investigate possible associations between QoL and characteristics of bowel nodules (dimension, location, and evidence of stenosis) as well as type and length of eventual medical therapy. Results Patients with BE had a satisfying overall QoL with a mean EHP-5 score of 105.42 ± 99.98 points and VAS lower than 3 for all pain domains; additionally, they did not show a significant impartment of bowel function with mean BENS score of 4.89 ± 5.28 points. Notably, patients under medical therapy had a better QoL than patients without therapy (p

Medical waste management is crucial issues for keeping our life safe and safety. Without effective measures for waste management, we cannot keep ourselves safe from different types of contagious diseases; viruses, bacteria. Knowledge sharing assists healthcare staff in identifying and mitigating hazards on appropriate handling, segregation and disposal medical waste. The aim of the study is to identify a multifaceted analysis of knowledge sharing, training, communication and attitudes for effective medical waste management. This study selected 153 medical workers randomly, including nurses, lab technologists, and cleaners, from hospitals, diagnostic centers, and clinics in the northern part of Bangladesh. The study design of this research is mixed-methods. This research adopts a survey and interview as a research strategy. The samples are collected randomly. SPSS software packages, MS Excel Office, and PLS-SEM are used to analyze data. Most of the respondents always share their knowledge face-to-face according to the findings. The result also shows that there are positive relationship between sources of knowledge (p: 0.046), awareness and knowledge (p: 0.049), attitudes of waste management (p: 0.025) and effective medical waste management. However, there are no relationships between communication and collaboration (p: 0.943), the practice of waste management (p: 0.155), supervision and staff monitoring (p: 0.527), training and education (p: 0.656) and effective medical waste management. Its focus on the northern part of Bangladesh introduces regional specificity, potentially limiting the applicability of results to other regions. The knowledge-sharing practices for waste management among medical staff are examined within this research, along with the relationship between attitude, awareness, and knowledge, staff supervision and monitoring, practice, training, and education and effective medical waste management that have not been studied before home and abroad. This analysis highlights the critical need for an integrated approach to medical waste management, emphasizing the importance of continuous education, open communication, and cultivating a culture that prioritizes environmental sustainability. The insights from this study offer practical recommendations for healthcare institutions to improve their medical waste management practices, ultimately leading to enhanced health outcomes and better environmental protection.

The burden of hypertension remains unacceptably high globally, particularly in low- and middle-income countries (LMICs). Workplace offer enormous potential as an idea setting for early detection and treatment of hypertension among the working class. Analysis of such a Workplace Screening Programme can reveal information about its potential impact. Machine learning techniques such as k-Means Clustering are untapped tools for such analyses. We set out to deploy this tool for the analysis of our university annual medical screening of workforce for hypertension. An anonymized dataset containing the demography and blood pressure measurements values obtained from staffs of different departments/units in an academic institution was obtained. The total number of samples or data points are 1, 723 in which the input dataset contains six features, including year category (2018, 2019, 2021,2022), Department/Unit (academic and non-academic), gender (male and female), while the target output is the blood pressure status (low, normal and high) respectively. Analysis of the dataset was carried out using machine learning techniques. In this retrospective analysis, it was observed that the mean age for this working class is 42 years old. Similarly, it was discovered that hypertension was prevalent among members of staff above the age of 40 irrespective of their gender or professional category (academic or non-academic). The analysis also revealed that there was a steady decline in the prevalence of hypertension from 2018 to 2022. From the research, it is evident that the adoption of machine learning techniques for periodic analyses of workplace health status screening initiative (especially for hypertension) is effective, feasible, and sustainable to diagnose and control hypertension among the working class.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

Smart electronic devices and telemedicine are widely used in humans' daily lives. This provides remote treatment of patients using information and communications technology. New telemedicine technologies, such as the Internet of Medical Things (IoMT), make it easier for medical and computing devices to communicate regularly and effectively. Critical motivations for adapting the IoMT are reduced cost, increased quality of life, and timely medical intervention. IoMT is significant because it enables continuous, real-time patient monitoring during routine everyday activities using a variety of wearables and sensors. With big data, the IoMT technology makes excellent use of Artificial Intelligence (AI) to support disease detection and health condition prediction, alerting patients and healthcare providers. Many research studies have been conducted to explore various aspects of IoMT and its applications in the real world. However, it is challenging to comprehend all the techniques and solutions proposed by the research community. Therefore, this survey sheds light on some crucial aspects of IoMT technologies and explores the potential research gaps and directions the research community could tackle. The survey examines and discusses the characteristics of IoMT standards, protocols, and types. It then delves into the layers of IoMT and distinguishes them into Fog and edge. The studies published under each type were explored, and the limitations of these works were highlighted. The research gaps and directions on IoMT approaches and technologies were also highlighted. With such findings and research directions, further research endeavors could be carried out to address the issues and existing limitations in the IoMT.

The knowledge of the precise anatomy and dimensions of the pulp chamber in the different teeth allows the odontologist to perform correct diagnosis, as well as treatment planning and moni-toring. The clinical practice has introduced cone-beam computed tomography (CB-CT) as a method to evaluate the morphology and dimensions of the pulp chamber. Nevertheless, micro-computed tomography (micro-CT) is regarded as the gold standard in approaching those topics. Here we have designed an algorithm that takes as input DICOM images from in vivo CB-CT of permanent molars to determine the accuracy CB-CT for evaluation of pulp chamber volume. The values were compared with those from in vivo CB-CT (rough and expert manipulated) and ex vivo micro-CT. The relative errors obtained with the volume calculated by the algorithm vs. the volume measured by micro-CT do not exceed 5.7%; also, no significant differences were found between algorithm volumes vs. manipulated CB-CT volumes while all volumes were different from those obtained with automatic CB-CT software. These data demonstrate that volume algorithm CB-CT based may be a reliable technique for evaluation of the pulp chamber volume in permanent molars and can be useful in the diagnosis of pulp diseases, as well as in the planning and monitoring of their treatments.

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and behavior. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated.

Extravasation, the unintended leakage of intravenously administered substances, poses significant challenges in cancer treatment, particularly during chemotherapy and radiotherapy. This comprehensive review explores the pathophysiology, incidence, risk factors, clinical presentation, diagnosis, prevention strategies, management approaches, complications, and long-term effects of extravasation in cancer patients. It also outlines future directions and research opportunities, including identifying gaps in current knowledge and proposing areas for further investigation in extravasation prevention and management. Emerging technologies and therapies with the potential to improve extravasation prevention and management in both chemotherapy and radiotherapy are highlighted. Such innovations include advanced vein visualization technologies, smart catheters, targeted drug delivery systems, novel topical treatments, and artificial intelligence-based image analysis. By addressing these aspects, this review not only provides healthcare professionals with insights to enhance patient safety and optimize clinical practice but also underscores the importance of ongoing research and innovation in improving outcomes for cancer patients experiencing extravasation events.

Wearable antennas are increasingly vital in modern healthcare, facilitating continuous monitoring and communication for medical purposes. These antennas are integrated into wearable devices, allowing for the transmission and reception of vital health data, such as heart rate, body temperature, and blood pressure, to medical professionals or monitoring systems in real time. This instantaneous data transmission enables timely interventions, thus improving the quality of healthcare services. This review aims to comprehensively synthesize recent research from reputable databases, focusing on the functionality and effectiveness of wearable antennas in medical applications. The primary objectives of each study were categorized, including antenna design and integration, the performance of medical wearable antenna technology, safety considerations in wearable antenna biomedicine, and the use of antennas in clinical applications and their impact. This synthesis provides valuable insights for researchers and practitioners aiming to develop and deploy wearable antennas in medical settings, ultimately enhancing healthcare delivery and patient outcomes.

The institutionalization of people with intellectual disabilities has been a common international response across cultures and generations that persists to this day. This chapter recounts its inheritance and manifestation in modern service provision. An institutional mind-set persists among clinicians and managers despite policies that emphasise the rights of persons with intellectual disability and anti-discriminatory practice. Alternative provision is rooted in person-centred practice; with a growing body of evidence for the improved quality of life that results from it. A reflective activity encourages readers to examine a recent case when they were associated with an institutional placement and what alternatives might have been sought. Yet alliances with other professionals, self-advocates and family carers will be needed to effect real and lasting changes in our service cultures and even then, the long shadows of institutional care may still persist.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Background: Obesity is a global health crisis with a rapidly increasing prevalence among university students. Medical students are a unique population that may be at a higher risk of obesity due to the demanding nature of their studies and the associated stress. Objective: This study aimed to determine the prevalence of overweight, obesity, and the associated risk factors among medical students at Jazan University in Saudi Arabia. Methods: A cross-sectional study was conducted among 228 medical students from their 2nd to 6th academic years at the Faculty of Medicine, Jazan University. A self-administered questionnaire was used to collect data regarding sociodemographic characteristics, physical activity, dietary habits, comorbidities, medication use, family history, and lifestyle factors. Anthropometric measurements including height, weight, and waist circumference were recorded. Chi-square test and binary logistic regression were used to identify the risk factors associated with obesity. Results: The prevalence of overweight and obesity among the participants was 13.3% and 15% respectively. Hence, the combined prevalence of overweight and obesity is 28.3%. The mean weight was 63.39 ± 18.93 kilograms, and the mean height was 163.48 ± 9.78 centimeters. On the other hand, 17.3% of participants had underweight whereas 54.4% had normal BMI. Most of the participants (61%) did not engage in regular exercise. A high proportion consumed fruits (82.9%) and vegetables (58.8%) 3 or fewer days per week, and 84.2% consumed 3 or fewer meals per day. Fast food consumption more than 3 days per week was reported by 42.1% of participants. Obesity was not significantly associated with sociodemographic factors, physical activity, dietary habits, comorbidities, medication use, or family histories. However, those with a monthly family income of 15,000-24,999 riyals had significantly lower odds of obesity than those in the lowest income group (OR 0.230, p = 0.045). Conclusion: The prevalence of overweight and obesity among medical students at Jazan University is high. Although no significant associations were found between obesity and most risk factors, this study highlights the need for interventions that promote healthy lifestyles among medical students. Further research is needed to identify effective strategies for preventing and managing obesity in this population.

Chronic kidney disease (CKD) is a progressive condition that affects more than 10% of the world's population. Monitoring urine albumin and creatinine ratio (µACR) has become the gold standard for nephropathy diagnosis and control. The objective of the present study was to develop a simple, accurate, sensitive, and rapid point-of-care test (PoCT) device - MYμALBUMIN that community healthcare professionals can use in resource-limited facilities and by patients in their own homes for screening the risk and monitoring the progress of CKD. Albumin and creatinine concentrations in urine samples were determined using spectrophotometric dye (tetrabromophenol blue)-binding and colorimetric Jaffe’assay, respectively. Urine samples were diluted with distilled water (1:80) and mixed separately with albumin and creatinine reaction mixture. The creatinine reaction was incubated at room temperature (25oC) for 30 minutes before analysis. Optical density (OD) was measured at the wavelengths of 625 nm (albumin) and 515 nm (creatinine). All calibration curves (2-60 mg/L and 0.25-2 mg/dL for albumin and creatinine) yielded linear relationships with correlation coefficients (r2) of &gt; 0.999. Good accuracy (%DMV &lt; + 2.88%) and precision (%CV &lt; 3.0%) were observed from both the intra- or interday assays for the determination of albumin and creatinine by MYµALBUMIN. The limit of quantification (LOQ) of albumin and creatinine in urine samples determined by MYµALBUMIN and laboratory spectrophotometer were 2 mg/L and 0.1 mg/dL, respectively, using 37.5 μL urine samples. The device was well applied with clinical samples from 13 CKD patients. The median (range) of %DMV of the central (hospital) laboratory method (immune-based assay) was 3.18 (-10.11 to 14.12)%, with a high correlation coefficient (r2 &gt;0.99). In conclusion, MYμALBUMIN showed satisfactory test performance in terms of accuracy, reproducibility, sensitivity, and specificity. The device would have significant economic benefits besides obviously improving clinical decision-making processes.

The landscape of medical education has witnessed significant transformations over the past decades, particularly with the advent of active teaching methodologies. However, despite these advancements, the traditional theoretical assessment methods have remained largely unchanged. This lack of evolution in assessment systems poses a challenge, as it is crucial for assessment methods to evolve in tandem with teaching approaches to ensure a comprehensive and effective learning process in medical education. This paper reviews the integration and effectiveness of open-book examinations (OBEs) in medical education, reflecting their growing significance. An integrative review of the literature was conducted, drawing from a range of relevant publications over the last decade, sourced from databases such as PubMed, Web of Science, Scopus, and ERIC. The inclusion criteria focused on full-text articles in English, with search terms including "medicine," "assessment," "open book examination," "open book exam," and "open book assessment," combined using Boolean operators. Thirteen publications were selected and critically appraised using The Critical Appraisal Skills Program checklist. The analysis identified three primary thematic categories: ' Teaching Strategy for Pandemic and Challenging Conditions,” Tool of Learning & Educational Impact” and “Operational Challenges & Future Directions”. These themes were explored to understand the role and impact of OBEs in medical education. The findings indicate that open-book examinations are a crucial component in the evolving landscape of medical education. While certain reservations remain, OBEs have shown significant potential in fostering critical thinking, argumentation skills, and lifelong learning among medical students. They reflect the ongoing evolution of knowledge in the medical field and contribute to the development of professionals’ adept at navigating and applying complex information. Further research is recommended to solidify these findings and to expand the understanding of OBEs in medical education.

1) Background: Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges are acknowledged; however, a comprehensive understanding of the status and the remedies remained unexplored. 2) Objective: This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. 3) Methods: A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history-taking, and attitudes toward adopting digital solutions for managing patient histories; 4) Results: 92.67% of the physicians face workloads, 88.46% struggle in medical history-taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. An AI-based Smart Health Gantt Chart (SHGC) system has been introduced for their instant feedback. 93.27% of physicians expressed their willingness to use such a system. 5) Conclusions: The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

Burnout represents a concern for all healthcare providers, particularly those specializing in emergency medical care for whom burnout outcomes have been well-documented. What remains unknown is the effect of burnout on redeployed medical specialists during the COVID-19 pandemic from an appointment-centered practice to emergency care directed by public health considerations. This research aims to identify and assess the burnout responses of fourteen medical specialties noted in searched returns of the four most cited articles published since 2020 about non-emergency physicians regarding their burnout brought on by the unanticipated need for them to provide emergency care during the recent pandemic. The hypothesis is that medical specialists accustomed to planning for emergency possibilities in their appointment-centered practice would demonstrate the least burnout regarding COVID-19-related emergencies. Considering coping as a process based on Lazarus's research, comparing and ranking the COVID-19 emergency responses across the various normally appointment-centered medical specialties in their employed coping strategies determines the outcome. With the hypothesis supported, suggested interventions for future pandemics—when these specialists are, again, redeployed to emergency care directed by public health considerations—are those coping strategies identified as most effective in reducing burnout while maintaining the viability of the medical specialty and excellent patient care.

Background: The clinical impact of withdrawal of sodium-glucose cotransporter 2 inhibitor (SGLT2i) on the all-cause readmission in patients with heart failure remains unknown. Methods: Consecutive 212 patients who were hospitalized for heart failure and received SGLT2i during the index hospitalization between February 2016 and July 2022 were evaluated. Of these, 51 patients terminated SGLT2i during or after their index hospitalization. The primary outcome was defined as an all-cause readmission rates/times. Results: Over a median of 23.2 months, an all-cause readmission occurred in 38 of 51 patients (74.5%) withdrawn from SGLT2i and 93 of 161 patients (57.8%) with continuation of SGLT2i (p = 0.099). The total number of all-cause readmissions per year was 0.97 [0-1.50] in patients withdrawn from SGLT2i and 0.50 [0-1.03] in patients with continuation of SGLT2i (p = 0.030). There was no significant difference in total medical cost (62,906 [502-187,246] versus 29,236 [7,920-180,305] JPY per month, p = 0.866) between both patient groups. Conclusions: Termination of SGLT2i may be associated with incremental all-cause readmission and no benefit in reducing total medical costs.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

Multivariate Time Series Classification (MTSC) is one of most important tasks in time series analysis, aiding in activities such as human motion recognition and medical diagnostics. Existing methods for MTSC do not explicitly model temporal differences and generalize the idea of temporal difference into a efficient temporal module. Additionally, existing methods are not yet able to capture cross-variable relationships well during network training. As a result, they are unable to achieve convincing feature representation, leading to suboptimal classification accuracy. In this paper, we propose a novel MTSC model called Temporal Difference and Cross-variate Fusion Network (TDCFN), which integrates a two-stream differential LSTM network and a cross-variate feature extraction network to enhance feature representation. TDCFN achieves superior classification accuracy by capturing dynamic temporal evolution and inter-variable relationships. The experimental results show that TDCFN can achieve competitive performance with state-of-the-art multivariate time series classification approaches.

The German health authorities' guidelines for medical devices in 2012 highlighted the importance of cleaning ultrasound probes, emphasizing their validation and reliability. In addition to automated and validated options, alternative manual methods such as wipe disinfection have gained traction due to their independence from additional hardware. The study examines the effectiveness of manual disinfection methods, specifically the cleaning process, addressing concerns raised by the Robert Koch Institute regarding the lack of validation for wipe disinfection of semi-critical devices. The EQUINOS colored wipe disinfection kit identified wetting gaps in all cleanings across four probes tested. The results indicate significant challenges in ensuring complete surface wetting, particularly in complex body parts such as clip-on areas and shapes for additional biopsy attachments, suggesting that manual methods alone may not adequately mitigate the risk of infection transmission (p value .001). The study concludes that while manual disinfection methods are a viable alternative to automated reprocessing, there is a critical need for enhanced training and potentially the development of more effective manual disinfection techniques or colored wipes to ensure patient safety and compliance with healthcare hygiene standards.

Background: Open AI developed ChatGPT, a language model based on the GPT architecture, designed for text-based communication. Trained on diverse internet texts, ChatGPT generates contextually appropriate responses using machine learning. It understands input through analysis and context interpretation, generating coherent and contextually relevant responses. Interaction is possible through messaging platforms. Materials and Methods: In November 2023, we utilized ChatGPT 3.5, the default version at that time, to answer questions from the Italian National Residency Program Admission Tests (SSMs) of 2021, 2022, and 2023. The questions cover clinical, diagnostic, analytical, therapeutic, and epidemiological scenarios, sometimes accompanied by images. The study compared ChatGPT's answers to the official corrections on the Italian Ministry of University and Research (MUR) website. The scoring method used for evaluation was 1 point for correct answers, 0 points for unanswered questions, and -0.25 points for incorrect answers, reflecting the SSM test scoring system. Results: In summary, ChatGPT was tested with a total of 420 questions, 140 for each test. It achieved an overall accuracy of 80.48%, providing correct answers for 338 questions and incorrect answers for 82 questions. When faced with questions containing both text and images, it answered 55% correctly and 45% incorrectly. The model's performance varied over time, with an 82.14% accuracy rate in 2021 and 2022 (115 correct out of 140) and a 77.14% accuracy rate in 2023 (108 correct out of 140). Applying thisscoring method to the SSM test, ChatGPT would have scored 105 points in 2021 and 2022, and 100 points in 2023. Conclusions: ChatGPT has exhibited above-average performance in the last three SSM tests, highlighting its robust capability to interpret clinical scenarios and offer precise diagnostic and therapeutic guidance. Despite this, some limitations persist, notably the software's inability to interpret non-textual information.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

Dermal injections of hyaluronic acid gel for aesthetic skin rejuvenation are increasingly popular nowadays. Although these products are classified as medical devices, the regulations on their administration by licensed practitioners are still weak, whereas their manufacturers increasingly highlight and advertise the cellular effects that underpin the efficacy of these injections. In this re-view, we discuss all current knowledge on the mode of action of dermally injected hyaluronic acid and the potential toxicological implications especially from crosslinked gels in conjunction with the current global regulations. We conclude that dermal injections of hyaluronic acid have several therapeutic implications that warrant further research, and that strict regulations must be applied on their manufacture/quality control and the required qualifications of licensed aesthetic injectors.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

Peritoneal fluid is an ovarian exudate from the growing follicle or corpus luteum. Therefore, the free concentrations of estrogens and progesterone are always much higher than in plasma, especially after ovulation. The peritoneum is not vascularised, and peritoneal implants and the superficial portion of deep endometriosis are likely influenced mainly by peritoneal fluid. Follicular phase progesterone concentrations are as high as during the luteal phase in plasma. This explains that most peritoneal implants are rarely proliferative and out of phase with the endometrium. From some 5 mm of depth onwards, endometriosis is rather influenced by plasma hormone concentra-tions, and the deeper parts can be proliferative as the endometrium. Because of these progesterone concentrations, progesterone resistance has to be postulated, permitting endometriosis lesions to initiate and develop. This opens the discussion of whether endometriosis is similar to the basal endometrium. These peritoneal fluid concentrations are important to understand the natural history and medical therapy of endometriosis. Most medical therapies decrease ovarian function and affect peritoneal fluid steroid hormone concentrations. It is suggested that the efficacy of medical therapy for endometriosis-associated pain is a consequence of the decreased estrogen concentrations in the peritoneal cavity and that the local effect of progestins may not be determinant for pain relief. If this is true, a history of endometriosis should not be considered a contraindication for hormone re-placement therapy.

The emergence of Internet of Things (IoT) devices is revolutionizing healthcare. This dynamic healthcare environment relies on Internet of Healthcare Things (IoHT) and Internet of Medical Things (IoMT) technologies. Economic, social, and technological opportunities from the IoHT are transforming healthcare systems. This article covers the history, present condition, and future prospects of IoHT, as well as its topologies, platforms, services, applications, market trends, and solution types. It examines security issues, requirements, and a threat taxonomy, studying smart healthcare privacy and security. It explores future research and the challenges IoHT-based solutions face. International health rules and regulations based on the IoT and their social and economic effects on long-term development are also examined. Integrating IoMT into healthcare infrastructures enhances scalability, efficiency, reliability, and accuracy. Through communication networks and data-processing algorithms, IoMT creates smart hardware and software platforms that enhance decision-making. IoMT devices' low resources raise privacy and security concerns. Security and privacy issues are organised by healthcare IoMT adoption in this study. We give an extensive attack taxonomy against the IoMT infrastructure, encompassing devices, security mechanisms, and their pros and cons. Furthermore, it describes the privacy and security requirements for any unique security solutions against IoMT's multiple attack vectors. This article explains the complicated difficulties, security concerns, and future research pathways needed to safeguard and ensure the long-term expansion of these game-changing technologies in the healthcare ecosystem.

The 2020 revision of South Korea’s Personal Information Protection Act (PIPA) updated procedures for the use of medical data for research. The PIPA provision allowing “pseudonymized information” to be used without the consent of the data subject for scientific research introduced ambiguity regarding the applicability of the PIPA versus the Bioethics and Safety Act (BSA), which allows “anonymized information” to be used for research only after data-subject consent and IRB approval for human-subject research are obtained. The main points requiring clarification include the distinction between “human-subject” and “scientific” research, the definition of anonymization and pseudonymization, the requirements for data-subject consent, and the procedures for institutional review boards (IRBs) and data review boards (DRBs). After reviewing the legal concepts and processes, this article recommends clarification of the guidelines for the use of medical data in research by distinguishing the concept and, in the long run, categorizing medical data based on the degree of risk.

Suboptimal knowledge of clinical pharmacology, therapeutics, and toxicology (CPT) and poor-quality prescribing are threats to patient safety. Our previous national survey of medical faculty identified limited confidence in medical student graduates’ ability to prescribe safely and an interest in a national prescribing competence assessment. Given the in-person challenges posed by the restrictions related to the COVID-19 pandemic, we aimed to re-evaluate opinions and gauge interest in e-learning resources and assessments. Using public sources, a sampling frame of medical school leaders from all 17 Canadian medical schools, including deans, vice-deans, and program directors for clerkship, residency, and e-learning, were invited to participate in a cross-sectional survey. Survey questions were finalized after several rounds of testing and analyses were descriptive.Of 1448 invitations, 411 (28.4%) individuals reviewed the survey, and among them, 278 (67.6%) completed at least one survey question with representation from all schools. While more than 90% of respondents agreed that medical students should meet a minimum standard of prescribing competence, only 17 (7.9%) could vouch for their school meeting objectives in CPT and many had significant concerns about their own or other schools’ recent graduate prescribing abilities. Given the lack of local CPT eCurricula resources, there was strong interest in a national online course and assessment in CPT. Our national survey results suggest an ongoing inadequacy of medical trainees’ prescribing competence and provide a strong endorsement for both a national online CPT course and assessment during medical school.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.

Keywords: Marine Coasts; Protected Areas; Defense and Conservation; Asbestos Cement Materials; Wastes; Contamination; Human Health Risk; Medical Geology.

In February 2004, the Food and Drug Administration (FDA) was the first to approve the combination of cisplatin (CDDP) and pemetrexed (PEM) as standard first-line chemotherapy for untreated, unresectable malignant pleural mesothelioma (MPM). However, after that approval, no progress was made in the standard first-line treatment of MPM for almost 15 years. Positive results from a phase 3 study (Mesothelioma Avastin Cisplatin Pemetrexed Study: MAPS) verifying the effect of bevacizumab, an anti-angiogenesis agent added to CDDP/PEM for unresectable MPM, were published in The Lancet in December 2015; however, this did not lead to approval by national drug regulatory agencies. Furthermore, no second-line treatment was established for cases refractory to CDDP/PEM. In August 2018, the Pharmaceuticals and Medical Devices Agency of Japan was the first in the world to approve monotherapy with nivolumab, an immune checkpoint inhibitor (ICI), for previously unresectable, advanced, or recurrent MPM. Following Japan, in October 2020, the FDA approved the combination of nivolumab and ipilimumab for the treatment of previously untreated, unresectable MPM. In this article, we review the transition of drug treatment for MPM, in light of the historical background, focusing on the benefits of ICIs.

The Work Ability Index (WAI) is the most widely used questionnaire for the self-assessment of working ability. Because of its different applications, shorter versions, and widespread use in healthcare activities, it is worthwhile assessing its characteristics. The WAI was distributed online among the workers of a healthcare company; the results were compared with data contained in the workers' personal health records and with absence registers. 340 out of 575 workers (59.1%) participated; 6.5% of them reported poor work ability. Exploratory factor analysis indicated that the one-factor version best described the characteristics of the WAI. The scores of the complete WAI, the shorter form without the list of diseases, and the minimal one-item version (WAS) had equal distribution and were significantly correlated. The WAI score was inversely related to age and significantly lower in women than in men, but it was higher in night workers than in colleagues due to the probable effect of selective factors. The WAI score was also correlated with absenteeism, but no differences were found between males and females in the average number of absences, suggesting that cultural or emotional factors influence the self-rating of the WAI. Workers tended to overreport illnesses in the online survey compared to data collected during occupational health checks. Musculoskeletal disorders were the most frequently reported illnesses (53%). Psychiatric illnesses affected 21% of workers and had the greatest impact on work ability. Multilevel ergonomic and human factor intervention seems to be needed to recover the working capacity of healthcare workers.

INTRODUCTION: With an increase in the aging population, the application of evidence-based practice in geriatric education can strengthen knowledge, skills, and clinical experience for healthcare students caring for older adults. Age Friendly Health Systems (AFHS) is one of the frameworks founded on providing evidence-based and low-risk care centered on what matters most to older adults, their families, and caregivers. Virtual Reality (VR) platforms are gaining popularity due to their ability to provide an immersive, hands-on learning experience resembling an actual medical practice or care setting. Immersive learning enhances students’ sensory perceptions, promoting an innovative and engaging way of acquiring concepts that are difficult to teach in real life. This study aimed to design, develop, implement, and evaluate a case-based training module highlighting AFHS and educating medical students on the 4Ms approach in geriatric care (What Matters, Medication, Mentation, and Mobility). METHODOLOGY: The project was a feasibility study completed in two phases. Phase one included planning and development of a case-based scenario incorporating 4Ms of AFHS. Phase two included implementing and evaluating the VR training module into the geriatric curriculum for medical students. RESULTS: The final VR case displays a hospital and post-acute setting where an elderly patient is admitted for a hip fracture. Students learn how to triage and treat patients from admission to discharge while demonstrating their knowledge of AFHS. Approximately 10% of students completed the evaluation survey, and preliminary results indicate significant knowledge change in pre-post scenario-based training on AFHS. CONCLUSION: The VR education platform and embedded scenario promise an innovative adaptation of technology in learning the concepts of the 4Ms of AFHS. However, future studies should explore VR education with clinical assessment evaluation to ensure competence in providing Age-Friendly care.

Objective: To use the ancient and modern medical record cloud platform (V2.3.5) to organize and explore the medication patterns of the "Three Causes, Extremity, and One Disease Syndrome Formula" by Chen Wuxuan, and to summarize its therapeutic ideas. Method: Collect and organize the "San Yin Si Tian Fang" in the "San Yin Ji Yi Zheng Fang Lun", and use the modern medical record cloud platform (V2.3.5) to conduct medication frequency statistical analysis, cluster analysis, and complex network analysis. As a result, a total of 16 Chinese herbal medicines were collected in the "Three Causes, Extreme and One Disease Syndrome Formula", involving 75 traditional Chinese medicines. Statistical analysis showed that roasted licorice was the most commonly used, followed by Pinellia ternata, papaya, ginger, and Poria cocos; Medications mainly include warm drugs, sweet and pungent flavors, as well as drugs that promote lung and spleen meridians. Cluster analysis can divide the above-mentioned traditional Chinese medicine into two groups, with specific grouping as follows. Group 1: ginger, white peony, aconite, papaya, and achyranthes bidentata; Group 2: roasted licorice, poria cocos, dried ginger, pinellia ternata, and green peel. Conclusion: The characteristics of the 16 prescriptions are biased towards Xin Gan Wen, emphasizing the regulation of the spleen and stomach, which may be related to the common use of prescription by doctors in the Song Dynasty and the tendency towards warm and dry medication.

The Internet of Things (IoT) has a growing presence in society's daily lives. These lightweight devices can be easily deployed and maintained, enabling extensive adoption in different environments. Furthermore, one of the most promising areas for using IoT devices is healthcare, comprising devices referred to as the Internet of Medical Things (IoMT). Several examples of healthcare services are supported by IoMT devices, e.g., continuous health monitoring. Conversely, there is an increasing concern with the cybersecurity aspects of these devices, and several attacks against IoT infrastructures have been launched in the past few years. These cybersecurity concerns also apply to healthcare applications, where the tradeoff between the benefits and security of IoMT devices must be observed. Given the complexity and amount of data IoMT network traffic generates, advanced methods become especially useful in these environments. Although Machine Learning (ML) brings various techniques and solutions to improve cyberattack detection, prevention, and mitigation, essential features are not addressed in the current state-of-the-art benchmark dataset contributions. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to enable the development and evaluation of IoMT security solutions. In order to accomplish this, 18 attacks were executed against an IoMT testbed composed of 40 IoMT devices (25 real devices and 15 simulated devices), considering the plurality of protocols used in healthcare (e.g., Wi-Fi, MQTT, and Bluetooth). These attacks are categorized into five classes: DDoS, DoS, Recon, MQTT, and spoofing. This effort aims to establish a baseline complementary to the state-of-the-art contributions. The outcome supports researchers in investigating and developing new solutions to make healthcare systems more secure using different mechanisms (e.g., machine learning - ML). This research goes beyond merely conducting attacks on IoMT devices. We also attempt to capture the lifecycle of these devices in different vital phases, from the moment they join the network until they leave, which is called profiling. Profiling allows the different classifiers to identify anomalies of each device individually in the healthcare network. The CICIoMT2024 dataset has been published on CIC's dataset page, making it available for other researchers to use.

Paramedics are increasingly being subjected to violence, creating the potential for significant physical and psychological harm. Where a patient has a history of violent behavior, hazard flags - applied either to the individual, their residential address, or phone number - can alert paramedics to the possibility of violence, potentially reducing the risk of injury. Leveraging a novel violence reporting process embedded in the electronic patient care record, we reviewed violence reports filed over a thirteen-month period since its inception in February 2021to assess the effectiveness of hazard flagging as a potential risk mitigation strategy. Upon reviewing a report, paramedic-supervisors can generate a hazard flag if recurrent violent behavior from the patient is anticipated. In all, 502 violence reports were filed, for which paramedic-supervisors generated hazard flags in 20% of cases (n=99). In general, cases were not flagged either because the incident occurred at a location not amenable to flagging, or because the supervisors felt that a hazard flag was not warranted based on the details in the report. Hazard flagging was associated with an increased risk of violence during subsequent paramedic attendance (Odds Ratio [OR] 6.21, p