Redesigning residency training with artificial intelligence: Implications for efficiency, quality, and workforce capacity

PURPOSE: Workforce shortages and increasing clinical demands in Radiation Oncology and Medical Physics present challenges for maintaining high-quality residency training. This work presents a conceptual framework to evaluate the potential role of artificial intelligence (AI) in improving efficiency, scalability, and quality of residency education. METHODS: Current teaching and learning modalities were categorized, and their relative contributions to training were estimated. Potential AI applications were mapped to these modalities, and the proportion of training that could be supported by AI was assessed. A simple conceptual model incorporating training time, AI-deliverable components (i.e., those potentially supported through direct interaction with AI systems), and dependence on direct human teaching was used to illustrate the potential impact on instructional effort. The potential effect of AI on educational quality was qualitatively evaluated, as well as limitations of AI for routine educational integration. RESULTS: AI applications were identified across multiple domains, including workflow-integrated feedback, simulation-based training, adaptive learning, and automated assessment. The largest potential impact was estimated in workflow-integrated, simulation-based, and self-directed learning environments. While reductions in direct human teaching effort were estimated to be modest, AI-enabled approaches were estimated to achieve substantial improvements in training quality, including increased feedback frequency, enhanced support for deliberate practice, and greater exposure to rare or high-risk scenarios. CONCLUSIONS: AI has the potential to enhance residency training by improving consistency, scalability, and educational quality. In the context of workforce constraints, the primary benefit of AI may be to augment training efficiency and reduce reliance on limited faculty resources while preserving the essential role of clinical experience.