Artificial Intelligence and Its Effect on Radiology Residency Education: Current Challenges, Opportunities, and...

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 article, 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 that offer education tools using 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 guarantee 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 postdeployment 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, use, and contribute to AI advancements, ensuring that these tools complement rather than undermine clinical expertise.