Cultural Adaptation of Attitude Scales Towards Artificial Intelligence Into Turkish

The rapid proliferation of artificial intelligence (AI) applications in healthcare has led to significant transformations, particularly in applications such as patient management, treatment planning, clinical decision support systems, and remote rehabilitation. Ensuring that this transformation is effective and safe requires the reliable measurement of patients' perceptions and attitudes toward AI-based health technologies. However, the existing literature does not include any scales developed to measure patients' attitudes toward AI that have been adapted to Turkish society. This situation complicates both the assessment of acceptance of AI in clinical applications and the sound execution of scientific research in this field.

This research aims to culturally adapt the internationally developed ATTARI-12 (Attitudes Toward Artificial Intelligence-12) and ATTARI-WHE (Artificial Intelligence in Work, Health and Everyday Life) scales into Turkish and to evaluate their construct validity and reliability. The study is methodological in design and will be conducted at the Faculty of Health Sciences, Izmir Katip Celebi University, between January 2026 and January 2027, following approval by the ethics committee. The cultural adaptation method proposed by Beaton and colleagues, which includes forward translation, back translation, expert panel, and content validity stages, will be applied during the scale adaptation process; then, the understandability of the items will be tested with a pilot application. The sample will consist of at least 200 physical therapy patients, and convergent validity, construct validity using confirmatory factor analysis, and reliability using Cronbach's alpha and test-retest methods will be evaluated.

The project will be carried out according to a structured schedule consisting of project management, translation process, pilot application, data collection, and analysis stages. All measurements will be performed after obtaining ethical committee approval.

The results of this study will contribute to the literature by providing patient-specific, valid, and reliable measurement tools that can be used to scientifically evaluate patients' attitudes toward AI in Turkey. These scales are expected to have a widespread impact in national research, in the evaluation of clinical decision support systems, and in strategies aimed at increasing the acceptance of AI-based health technologies.