Volin Joshua, van Assen Marly, Bala Wasif, Safdar Nabile, Balthazar Patricia
Resident, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia; Chief Resident of Diagnostic Radiology, Emory University.
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia; Co-Chair of the Imaging Informatics Research Team.
J Am Coll Radiol. 2025 Nov;22(11):1264-1270. doi: 10.1016/j.jacr.2025.07.004. Epub 2025 Jul 12.
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.
人工智能已成为放射学领域一股令人瞩目的力量，它改善了工作流程，并影响着临床决策。随着其应用日益广泛，有必要更深入地探讨住院医师如何能恰当地接触到这项技术。在本文中，我们旨在讨论对于学员体验至关重要的三大支柱，包括人工智能教育、人工智能教育工具以及人工智能的临床应用。本就过度拥挤的临床住院医师课程几乎没有空间进行全面的人工智能教育，这一挑战或许可通过住院期间的纵向差异化教育路径或各学会提供的外部课程来克服。除了教授人工智能的基础知识外，提供使用人工智能的教育工具的项目将改进过时的临床课程。这些教育工具在研究和行业中是一个不断发展的领域，提供了各种独特的机会来促进主动探究、提高理解能力以及提升整体临床能力。近700种经美国食品药品监督管理局（FDA）批准的人工智能临床工具几乎可以肯定住院医师将会接触到这项技术，这对教育可能会产生好坏参半的影响，不过还需要更多研究来进一步阐明这一挑战。伦理考量，包括算法偏差、责任和部署后监测，凸显了结构化教学和指导的必要性。随着人工智能不断发展，住院医师培训项目必须优先制定基于证据、适应性强的课程，以使未来的放射科医生能够批判性地评估、使用人工智能并为其发展做出贡献，确保这些工具能辅助而非削弱临床专业知识。
