Measuring AI acceptance among Japanese medical students and residents

Artificial intelligence (AI) is rapidly transforming healthcare and medical education. From improving diagnostic accuracy and clinical decision-making to enabling virtual simulations and personalized learning, AI technologies are being integrated into the daily practice of clinicians and residents. Despite these benefits, concerns remain regarding ethical liability, data privacy, loss of human autonomy, and potential job displacement. As AI continues to expand into healthcare systems around the world, it is increasingly important to understand how future physicians will perceive and utilize these technologies.

Attitudes toward AI play a key role in determining whether AI tools are accepted, trusted, and effectively integrated into clinical practice and education. Positive attitudes promote openness and responsible use, while negative perceptions can lead to skepticism and underutilization. Therefore, accurately measuring medical students’ and residents’ attitudes toward AI is essential to identifying barriers to adoption and designing effective educational interventions. In 2024, Stein et al. introduced the 12-item Attitudes Toward Artificial Intelligence (ATTARI-12) scale, a brief and reliable measure that covers affective, cognitive, and behavioral aspects. However, the lack of a validated Japanese version has limited its application in Japan, where cultural factors such as uncertainty avoidance and social norms can influence responses to emerging technologies.

To address this gap, a research team led by Project Assistant Professor Hirohisa Fujikawa and Dr. Hirotake Mori, Dr. Yuji Nishizaki, Dr. Yuichiro Yano, and Dr. Toshio Naito from Juntendo University in Japan collaborated with Dr. Kayo Kondo from Durham University in the UK. Together, they developed and validated a Japanese version of the scale (J-ATTARI-12) for use among medical students and residents. Dr. Fujikawa explained the motivation behind the study:We observed large variations in how learners responded to AI, but no validated tools existed in Japan to measure these differences. This scale helps educators understand learner attitudes and better prepare future physicians for AI-powered practiceTheir findings were published in the journal Volume 12, Issue e81986. JMIR Medical Education January 14, 2026.

This study was conducted in accordance with internationally recognized guidelines for translation and cross-cultural adaptation to ensure linguistic accuracy and cultural relevance. The nationwide online survey was conducted from June to July 2025 among medical students and residents at multiple universities and hospitals across Japan. A total of 326 participants were included in the analysis. A split-half validation approach was adopted for the psychometric evaluation. Briefly, exploratory factor analysis (EFA) was performed on half of the sample to identify the underlying factor structure, and confirmatory factor analysis (CFA) was performed on the other half to assess model fit. Convergent validity was tested by correlating J-ATTARI-12 scores with a related construct, attitude toward robots, and internal consistency reliability was assessed using Cronbach’s α.

The analysis yielded several important findings. EFA identified a two-factor structure reflecting “AI anxiety and aversion” and “AI optimism and acceptance.” CFA demonstrated that this two-factor model showed good model fit and performed better than the one-factor model. Convergent validity was supported by a moderate positive correlation between J-ATTARI-12 scores and attitudes toward robots, and internal consistency reliability was also high, indicating that this scale reliably measures Japanese residents’ attitudes toward AI.

This study has important implications for education and research. Dr. Fujikawa said:Educators can use this scale to evaluate AI-related training and identify learners who are anxious or hesitant about using AI. Researchers can also track how attitudes evolve as AI is further integrated into healthcare.J-ATTARI-12 supports data-driven curriculum development and informed decision-making in medical education by providing a culturally adaptive and psychometrically sound instrument.

Reflecting on its broader significance, Dr. Fujikawa emphasized:The successful implementation of AI in healthcare will depend as much on clinician acceptance as on technical performance. Making these attitudes visible allows for better education and more responsible implementation.He added that the scale will be used in the “Medicine and AI” program to be launched at Juntendo University in 2026, and is expected to facilitate future cross-border research.

In conclusion, this study successfully developed and validated the J-ATTARI-12, the first tool in Japan to assess medical students’ and residents’ attitudes toward AI. By providing a reliable and valid measure, we build a strong foundation for advancing AI education, research, and integration within Japan’s medical training system.