Expert Consensus on the Artificial Intelligence Proficiency Competency List and Assessment Framework for Medical Students (2025 Edition).

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.

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Publication Details

Journal
PubMed
Published
2026-02-01
DOI
https://doi.org/10.3881/j.issn.1000-503x.17210
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Expert Consensus on the Artificial Intelligence Proficiency Competency List and Assessment Framework for Medical Students (2025 Edition).

Kuang Tong, Yunfei Hou, Li Hong, Xun-Ming Ji et al.
PubMed
Artificial Intelligence in Healthcare and Education
article

Expert Consensus on the Artificial Intelligence Proficiency Competency List and Assessment Framework for Medical Students (2025 Edition).

Kuang Tong, Yunfei Hou, Li Hong, Xun-Ming Ji, Bo-Han Zhang, Hui Pan, Yong-Hui Ma, Jiao Li, Zhi-Rong Zeng, Hui Liu, Meng-Chun Gong, Wei Chen, Bo Zhang
article en

Abstract

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.

PubMedVol. 48(1)
Sun Yat-sen University (CN), Guangdong Medical College (CN), Shanghai Jiao Tong University (CN), Xiamen University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking University (CN), Shaoguan University (CN), Shanghai Children's Medical Center (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Tongji Hospital (CN), Huazhong University of Science and Technology (CN), Xiamen University of Technology (CN)
Quality Education
Openalex Percentile: Top 67%
Artificial Intelligence in Healthcare and Education
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