JMIR Publications

ABSTRACT

Background:

In the current era of artificial intelligence (AI), utilization 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 Attitude towards Artificial Intelligence (ATTARI-12) scale has demonstrated good validity and reliability, suggesting its potential for extensive utilization 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 healthcare and medical education system.

Objective:

We aimed to develop and validate the Japanese version of the ATTARI-12 (J-ATTARI-12) scale.

Methods:

We first translated the original English scale into Japanese in accordance with an international guideline. To examine its psychometric properties, we then conducted a validation survey by distributing the translated version as an anonymous online self-administered questionnaire to medical students and residents across Japan from June to July 2025. We assessed structural validity through factor analysis, and convergent validity by computing the Pearson correlation coefficient between the J-ATTARI-12 scale scores and the attitudes towards robots scores. Internal consistency reliability was assessed by Cronbach’s alpha values.

Results:

We included 326 participants in our analysis. We employed a split-half validation approach, with exploratory factor analysis (EFA) on the first half and confirmatory factor analysis (CFA) on the second. EFA suggested a two-factor solution. CFA revealed that the model fitness indices of the two-factor structure suggested by the EFA was 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 one-factor structure. The value of the Pearson correlation coefficient between the J-ATTARI-12 scale scores and attitudes towards robots scores was 0.52, which indicated good convergent validity. Cronbach’s alpha value 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 scale. The developed instrument had good structural validity, convergent validity, and internal consistency reliability. The J-ATTARI-12 scale 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.