The Role of Artificial Intelligence in Radiation Therapy Treatment Planning: A Comprehensive Review

This comprehensive review provides an in-depth analysis of the advancements and potential applications of artificial intelligence (AI) in the field of medical physics. The integration of AI techniques holds great promise for transforming various aspects of medical physics practice, ranging from image analysis and interpretation to treatment planning and optimization. Drawing upon a wide range of research studies and literature from the medical physics domain, this review examines the utilization of AI algorithms and approaches in enhancing accuracy, efficiency, and automation in medical physics. Machine learning, deep learning, and computer vision techniques are explored in the context of image analysis, where AI algorithms demonstrate the ability to detect and classify abnormalities, segment organs and tumors, and aid in image registration and reconstruction. In treatment planning and optimization, AI algorithms are being employed to optimize radiation therapy plans, taking into account patient-specific characteristics, tumor response, and organ-at-risk constraints. These algorithms leverage large datasets and sophisticated optimization algorithms to generate personalized treatment plans that maximize tumor control while minimizing adverse effects on healthy tissues. Quality assurance, an essential aspect of medical physics, is also benefiting from AI advancements. AI-based tools are being developed to automate quality assurance processes, including the verification of treatment delivery accuracy, patient positioning, and dose calculation. These tools can help improve efficiency, reduce human error, and enhance patient safety in radiation therapy.