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A Chinese Expert Consensus on the Artificial Intelligence Proficiency of Medical Students: Competencies and the
Mengchun Gong1,2,3, Jiao Li4, Yonghui Ma5
1GMC Lab, School of Biomedical Engineering Guangdong Medical University Dongguan China.
Background:
Artificial intelligence (AI) is transforming healthcare, demanding reevaluation of medical education. China's "New Medical Education" initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards, rapid technological evolution, and insufficient localized ethical norms.
Objective:
To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.
Methods:
A multidisciplinary (including medical education, clinical medicine, medical AI, public health, and medical ethics) expert group (n = 32) developed an initial competency list based on the "Knowledge-Skills-Attitude" Medical Competency Model. Two Delphi rounds (100% response rate; consensus threshold: mean ≥ 4.0, CV ≤ 0.25) refined the framework. Core competencies were prioritized via Analytic Hierarchy Process (AHP). The final consensus document was established after multiple expert group meetings.
Results:
The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into professional knowledge, clinical practice, research, and health management. It comprises a 21-item Competencies of AI Proficiency (CAIP) list across knowledge (eight indicators), skills (seven indicators), and attitude (six indicators) dimensions. Key competencies prioritized include understanding AI's role in multidisciplinary knowledge integration (CAIP3), identifying AI output biases (CAIP4), understanding health data governance (CAIP2), maintaining physician-led AI-assisted diagnosis (CAIP16), and identifying AI diagnostic biases (CAIP12). A multi-modal assessment framework is recommended, including paper-based/computerized tests for knowledge, situational judgment tests (SJTs) for attitudes, and objective structured clinical examinations (OSCEs) with a specific "AI Clinical Decision Conflict Scoring Scale" for skills. A multi-stage dynamic assessment system ("Pre-enrollment-Pre-clinical-Post-clinical") is proposed for longitudinal tracking. Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years, constructing an integrated curriculum covering fundamental principles, advanced large model applications (e.g., prompt engineering, agent development), and ethical considerations, supported by a "digital twin hospital platform."
Conclusion:
This consensus provides authoritative, China-specific guidance for defining and assessing medical students' AI literacy, adhering to national policies and regulations. It offers a core action framework for optimizing AI integration into medical education, fostering future healthcare professionals proficient in both AI technology and medical humanism, with a commitment to dynamic updating to adapt to evolving AI advancements.