ArticleHealth care science2026
A Chinese Expert Consensus on the Artificial Intelligence Proficiency of Medical Students: Competencies and the Multi-Modal Assessment.
Article in Health care science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.Frontiers in public health · 2026Article
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Authors and funding
11 authors.
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Abstract
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 ( 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.
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