Japanese Medical Trainees’ Perspectives on Artificial Intelligence in
Japanese Medical Trainees’ Perspectives on Artificial Intelligence in
Publish Date: 2026-02-24 08:33:00
Source Domain: bioengineer.org
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Culturally Adapted AI Attitudes Scale: Japanese and British researchers developed a culturally and linguistically adapted version of the 12-item Attitudes Towards Artificial Intelligence scale (ATTARI-12) for medical students and resident physicians in Japan, named J-ATTARI-12.
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Psychometric Validity: The study rigorously validated the J-ATTARI-12 to ensure it accurately captures nuanced attitudes towards AI in medical education through factor analysis showing a two-factor structure: “AI anxiety and aversion” vs. “AI optimism and acceptance.”
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Extensive Validation Process: The research involved comprehensive survey methods, including multicenter sampling of 326 trainees, exploratory and confirmatory factor analysis, and correlations with attitudes towards robots to assess convergent validity.
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Educational and Societal Implications: The J-ATTARI-12 serves as an essential tool in medical education, facilitating targeted interventions to foster responsible and effective adoption of AI technologies by trainees.
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Cross-Cultural and Collaborative Research: This work emphasizes cross-national collaboration and setting a precedent for culturally adapting global AI attitude scales, allowing for comparative studies and a broader dialogue on AI attitudes in healthcare.
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Longitudinal and Policy Insights: The tool helps track attitudes over time, providing empirical data for curriculum development and policy-making around AI education and ethical integration in medical practice.
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Future Educational Integration: The instrument will be integrated into Juntendo University’s new “Medicine and AI” program aimed at preparing future clinicians to be competent in AI-enabled practices.
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Cultural Dynamics and Human Factors: The study highlights cultural nuances within Japanese medical trainees and provides insights into how cultural influences affect acceptance and integration of AI in healthcare.
