Version 1
: Received: 6 March 2024 / Approved: 7 March 2024 / Online: 7 March 2024 (11:59:44 CET)
How to cite:
Odah, M. Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current Trends. Preprints2024, 2024030433. https://doi.org/10.20944/preprints202403.0433.v1
Odah, M. Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current Trends. Preprints 2024, 2024030433. https://doi.org/10.20944/preprints202403.0433.v1
Odah, M. Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current Trends. Preprints2024, 2024030433. https://doi.org/10.20944/preprints202403.0433.v1
APA Style
Odah, M. (2024). Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current Trends. Preprints. https://doi.org/10.20944/preprints202403.0433.v1
Chicago/Turabian Style
Odah, M. 2024 "Artificial Intelligence (AI) and Machine Learning (ML) in Diagnosing Cancer: Current Trends" Preprints. https://doi.org/10.20944/preprints202403.0433.v1
Abstract
Cancer diagnosis stands at the cusp of a profound transformation, driven by the burgeoning capabilities of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review illuminates the extraordinary impact of AI and ML in the field, unraveling their multifaceted roles in the realm of oncology. In the arena of cancer diagnosis, AI and ML serve as invaluable allies, empowering healthcare professionals with unparalleled tools for precision and efficiency. Notably, AI's prowess in analyzing medical images, including radiological scans and pathology slides, elevates the early detection of malignancies to new heights. Coupled with its ability to dissect genomic data, AI tailors therapeutic strategies to the individual, promising optimized treatment outcomes. However, the incorporation of AI and ML into clinical practice necessitates a careful navigation of ethical considerations, data privacy, and regulatory landscapes. Safeguarding patient data, ensuring transparency, and addressing algorithmic biases emerge as pivotal challenges that require vigilant attention. Yet, the future of AI and ML in cancer diagnosis is brimming with promise. The integration of multi-modal data, real-time monitoring, and Explainable AI (XAI) methods holds the potential to enrich diagnostic capabilities and engender patient trust. Global collaboration and data sharing initiatives are fostering the development of robust AI models. Furthermore, AI's role in cancer prevention, identifying high-risk individuals and enabling targeted preventive strategies, is poised to revolutionize healthcare. AI and ML are forging a path toward an era of unparalleled accuracy, efficiency, and personalization in cancer diagnosis. Challenges notwithstanding, these technologies bear the promise of fundamentally reshaping patient care, elevating outcomes, and advancing the fight against cancer. The ongoing exploration and responsible implementation of AI and ML in oncology will be pivotal in harnessing their full potential and paving the way for a brighter future for cancer patients.
Keywords
Artificial Intelligence; Machine Learning; Cancer Diagnosis; Precision Medicine; Data Privacy; Regulatory Frameworks
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
