Venugopal Vasantha Kumar, Kumar Amit, Tan Min On, Szarf Gilberto
Amrita Vishwa Vidyapeetham University, Coimbatore, India.
Chettinad Academy of Research and Education, Chennai, India.
Abdom Radiol (NY). 2025 Jun 9. doi: 10.1007/s00261-025-05016-5.
The exponential rise in the artificial intelligence (AI) tools for medical imaging is profoundly impacting the practice of radiology. With over 1000 FDA-cleared AI algorithms now approved for clinical use-many of them designed for radiologic tasks-the responsibility lies with training institutions to ensure that radiology residents are equipped not only to use AI systems, but to critically evaluate, monitor, respond to their output in a safe, ethical manner. This review proposes a comprehensive framework to integrate AI into radiology residency curricula, targeting both essential competencies required of all residents, optional advanced skills for those interested in research or AI development. Core educational strategies include structured didactic instruction, hands-on lab exposure to commercial AI tools, case-based discussions, simulation-based clinical pathways, teaching residents how to interpret model cards, regulatory documentation. Clinical examples such as stroke triage, Urinary tract calculi detection, AI-CAD in mammography, false-positive detection are used to anchor theory in practice. The article also addresses critical domains of AI governance: model transparency, ethical dilemmas, algorithmic bias, the role of residents in human-in-the-loop oversight systems. It outlines mentorship, faculty development strategies to build institutional readiness, proposes a roadmap to future-proof radiology education. This includes exposure to foundation models, vision-language systems, multi-agent workflows, global best practices in post-deployment AI monitoring. This pragmatic framework aims to serve as a guide for residency programs adapting to the next era of radiology practice.
用于医学成像的人工智能（AI）工具呈指数级增长，正在深刻影响放射学实践。目前已有1000多种获得美国食品药品监督管理局（FDA）批准用于临床的AI算法，其中许多是为放射学任务设计的，培训机构有责任确保放射科住院医师不仅有能力使用AI系统，而且能够以安全、合乎道德的方式对其输出进行批判性评估、监测和回应。本综述提出了一个将AI整合到放射科住院医师课程中的综合框架，目标是培养所有住院医师所需的基本能力，以及为那些对研究或AI开发感兴趣的人提供可选的高级技能。核心教育策略包括结构化的理论教学、亲身体验商业AI工具的实验室实践、基于案例的讨论、基于模拟的临床路径、教授住院医师如何解读模型卡片和监管文件。通过中风分诊、尿路结石检测、乳腺X线摄影中的AI辅助检测（AI-CAD）、假阳性检测等临床实例将理论应用于实践之中。本文还讨论了AI治理的关键领域：模型透明度、伦理困境、算法偏差、住院医师在人在回路监督系统中的作用。它概述了指导、教师发展策略以建立机构准备状态，提出了使放射学教育面向未来的路线图。这包括接触基础模型、视觉语言系统、多智能体工作流程、部署后AI监测的全球最佳实践。这个实用的框架旨在为适应放射学实践新时代的住院医师培训项目提供指导。
