To cultivate composite medical professionals capable of adapting to the development of intelligent healthcare,this consensus is grounded in the competency-based medical education,integrating the competency model and Miller's pyramid of clinical competence. A two-round Delphi method involving a multidisciplinary expert panel was conducted,combined with a systematic literature review,to develop a 21-indicator artificial intelligence(AI) literacy competency list for medical students across three domains:knowledge (8 indicators),skills (8 indicators),and attitudes (5 indicators). Furthermore,the consensus proposes a practical assessment system:standardized testing for the knowledge domain,situational judgment tests for the attitudes domain,and objective structured clinical examinations incorporating AI-related scenarios for the skills domain. In addition,a longitudinal assessment strategy spanning the phases of admission,preclinical training,and clinical training is recommended. The competency list and assessment framework established in this consensus demonstrate strong scientific rigor,authority,and practical applicability,and can serve as an important reference for medical schools seeking to advance the deep integration of AI and medical education and to cultivate composite medical talents suited to the era of intelligent healthcare.
Gong et al. (Fri,) studied this question.