Version 1
: Received: 13 August 2023 / Approved: 14 August 2023 / Online: 14 August 2023 (08:38:02 CEST)
How to cite:
Kouhen, F.; El gouach, H.; Saidi, K.; Errafiy, N.; Dahbi, Z. Synergizing Expertise and Technology: The AI Revolution in Radiotherapy for Personalized and Precise Cancer Treatment. Preprints2023, 2023080992. https://doi.org/10.20944/preprints202308.0992.v1
Kouhen, F.; El gouach, H.; Saidi, K.; Errafiy, N.; Dahbi, Z. Synergizing Expertise and Technology: The AI Revolution in Radiotherapy for Personalized and Precise Cancer Treatment. Preprints 2023, 2023080992. https://doi.org/10.20944/preprints202308.0992.v1
Kouhen, F.; El gouach, H.; Saidi, K.; Errafiy, N.; Dahbi, Z. Synergizing Expertise and Technology: The AI Revolution in Radiotherapy for Personalized and Precise Cancer Treatment. Preprints2023, 2023080992. https://doi.org/10.20944/preprints202308.0992.v1
APA Style
Kouhen, F., El gouach, H., Saidi, K., Errafiy, N., & Dahbi, Z. (2023). Synergizing Expertise and Technology: The AI Revolution in Radiotherapy for Personalized and Precise Cancer Treatment. Preprints. https://doi.org/10.20944/preprints202308.0992.v1
Chicago/Turabian Style
Kouhen, F., Nadia Errafiy and Zineb Dahbi. 2023 "Synergizing Expertise and Technology: The AI Revolution in Radiotherapy for Personalized and Precise Cancer Treatment" Preprints. https://doi.org/10.20944/preprints202308.0992.v1
Abstract
In recent years, the field of radiotherapy has witnessed remarkable advancements with the integration of artificial intelligence (AI) technologies into clinical practice.
Traditionally, radiotherapy treatment planning has been a labor-intensive process, requiring meticulous manual segmentation and optimization. With the advent of AI, particularly deep learning algorithms, the accuracy and efficiency of target delineation and organ-at-risk segmentation have significantly improved. AI-driven algorithms analyze voluminous medical imaging data, enabling rapid and precise contouring, thus expediting the planning phase and reducing inter-observer variability.
Furthermore, AI's role extends to treatment plan optimization, where it intelligently explores vast parameter spaces to generate optimal plans tailored to individual patients. This not only saves dosimetrists' time but also enhances plan quality by accounting for complex anatomical variations and personalized clinical goals.
In the realm of treatment delivery, AI-powered real-time image guidance enhances the accuracy of patient positioning, ensuring precise radiation targeting. Adaptive radiotherapy, enabled by AI, allows on-the-fly plan modifications in response to anatomical changes, significantly improving treatment accuracy in scenarios like tumor shrinkage or weight loss.
Beyond planning and delivery, AI algorithms contribute to outcome prediction by analyzing historical patient data and treatment responses. This predictive capability aids clinicians in making informed decisions and refining treatment strategies for better prognoses.
Despite the revolutionary potential, challenges remain in seamlessly integrating AI into clinical workflows. Ethical considerations, data privacy, and algorithm interpretability necessitate cautious implementation. Additionally, fostering interdisciplinary collaboration between AI experts and radiation oncologists is imperative to harness the technology's full potential.
This paper explores the impact of AI in four key areas of radiotherapy: automated segmentation, dosimetric and machine quality assurance, adaptive radiation therapy, and clinical outcome prediction.
Medicine and Pharmacology, Oncology and Oncogenics
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.
