放射学学习与发展当前议题：AJR专家小组叙述性综述

Radiology learning and development-encompassing both trainee education and lifelong professional learning-are undergoing rapid transformation driven by sustained growth in imaging volumes, persistent workforce constraints, increasing subspecialization, and evolving hybrid practice models that are reshaping radiologists' work. The resulting pressures increasingly and directly compete with protected time for case-based teaching, mentorship, feedback, and deliberate practice, placing at risk the entire professional development cycle from training through practice. Meanwhile, virtual and asynchronous educational platforms are expanding access to teaching conferences, case libraries, mobile resources, microlearning opportunities, and global collaboration and introducing challenges related to engagement, curation, feedback, and procedural skill expertise. Peer-learning programs are also gaining momentum as an alternative to traditional score-based peer review, offering a structured nonpunitive approach to shared learning and practice improvement embedded within routine clinical work. In parallel, generative artificial intelligence and large language models are creating new opportunities for knowledge synthesis and educational support amidst important concerns regarding accuracy, bias, outdated information, and overreliance. In this AJR Expert Panel Narrative Review, we examine how these converging trends are reshaping radiology learning and development and provide consensus guidance for strengthening educational quality while sustaining diagnostic excellence and professional growth in contemporary practice.