Adaptation of the Japanese Version of the 12-Item Attitudes Towards Artificial Intelligence Scale for Medical Trainees: Multicenter Development and Validation Study.

Background: In the current era of artificial intelligence (AI), use of AI has increased in both clinical practice and medical education. Nevertheless, it is probable that perspectives on the prospects and risks of AI vary among individuals. Given the potential for attitudes toward AI to significantly influence its integration into medical practice and educational initiatives, it is essential to assess these attitudes using a validated tool. The recently developed 12-item Attitudes Towards Artificial Intelligence scale has demonstrated good validity and reliability for the general population, suggesting its potential for extensive use in future studies. However, to our knowledge, there is currently no validated Japanese version of the scale. The lack of a Japanese version hinders research and educational efforts aimed at understanding and improving AI integration into the Japanese health care and medical education system. Objective: We aimed to develop the Japanese version of the 12-item Attitudes Towards Artificial Intelligence scale (J-ATTARI-12) and investigate whether it is applicable to medical trainees. Methods: We first translated the original English-language scale into Japanese. To examine its psychometric properties, we then conducted a validation survey by distributing the translated version as an online questionnaire to medical students and residents across Japan from June 2025 to July 2025. We assessed structural validity through factor analysis and convergent validity by computing the Pearson correlation coefficient between the J-ATTARI-12 scores and scores on attitude toward robots. Internal consistency reliability was assessed using Cronbach α values. Results: We included 326 participants in our analysis. We used a split-half validation approach, with exploratory factor analysis (EFA) on the first half and confirmatory factor analysis on the second half. EFA suggested a 2-factor solution (factor 1: AI anxiety and aversion; factor 2: AI optimism and acceptance). Confirmatory factor analysis revealed that the model fitness indexes of the 2-factor structure suggested by the EFA were good (comparative fit index=0.914 [>0.900]; root mean square error of approximation=0.075 [<0.080]; standardized root mean square residual=0.056 [<0.080]) and superior to those of the 1-factor structure. The value of the Pearson correlation coefficient between the J-ATTARI-12 scores and the attitude toward robots scores was 0.52, which indicated good convergent validity. The Cronbach α for all 12 items was 0.84, which indicated a high level of internal consistency reliability. Conclusions: We developed and validated the J-ATTARI-12. The developed instrument had good structural validity, convergent validity, and internal consistency reliability for medical trainees. The J-ATTARI-12 is expected to stimulate future studies and educational initiatives that can effectively assess and enhance the integration of AI into clinical practice and medical education systems.