Artificial Intelligence and its effect on Radiology Residency Education: Current Challenges, Opportunities, and Future Directions.

Abstract

Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision-making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this paper, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI-Education tools, and clinical implementation of AI. An already overcrowded clinical residency curricula makes little room for a thorough AI education; the challenge of which may be overcome through longitudinal distinct educational tracks during residency or external courses offered through a variety of societies. In addition to teaching the fundamentals of AI, programs which offer education tools utilizing AI will improve on antiquated clinical curricula. These education tools are a growing field in research and industry offering a variety of unique opportunities to promote active inquiry, improved comprehension and overall clinical competence. The near 700 FDA-approved AI clinical tools almost guarantees that residents will be exposed to this technology which may have mixed effects on education, although more research needs to be done to further elucidate this challenge. Ethical considerations, including algorithmic bias, liability, and post-deployment monitoring, highlight the need for structured instruction and mentorship. As AI continues to evolve, residency programs must prioritize evidence-based, adaptable curricula to prepare future radiologists to critically assess, utilize, and contribute to AI advancements, ensuring that these tools complement rather than undermine clinical expertise.