Enhancing Radiation Oncology Education Through Artificial Intelligence: A Review of Applications, Limitations, and...

Artificial intelligence [AI] is increasingly integrated into radiation oncology practice, from auto-contouring and treatment planning to decision support. However, formal residency training has not kept pace with these advances, leaving educational gaps in preparing future radiation oncologists for AI-informed clinical practice. This review aims to review current applications of AI in radiation oncology and evaluate how AI-driven tools can enhance resident education in clinical, procedural, and research domains. A narrative literature review was conducted across major databases [MEDLINE, EMBASE, CENTRAL, CINAHL] using keywords including "artificial intelligence," "medical education," "radiation oncology," and "auto-contouring." Expert commentary and selected studies on educational implementation of AI were included. AI enhanced learning tools span auto-segmentation feedback systems, plan optimization simulators and clinical decision support engines. AI improves access to complex cases, supports real-time feedback, and reduces dependence on faculty availability. However, risks include overreliance, algorithmic bias, and misinterpretation of AI generated outputs. Residents must develop the skills to critically appraise AI tools, review outputs, and integrate patient-centered decision making. AI offers significant potential to transform resident education in radiation oncology. Structured curriculum integration can enhance training while preserving core clinical judgment. Faculty development and institutional support are critical to successful implementation.