Perceptions of Dentists Towards Artificial Intelligence: Validation of a New Scale

Abstract

Objectives: To enhance the effectiveness and efficiency of using artificial intelligence (AI) in healthcare, it is crucial to comprehend the perceptions of healthcare professionals and individuals regarding AI. This study aimed to: (i) develop and conduct psychometric analyses of a new measurement tool, the AI Perceptions Scale (AIPS); and (ii) identify and compare sub-dimensions of perceptions of AI and its sub-dimensions, specifically in the dental profession. Materials and Methods: The study used a cross-sectional and correlational design involving 543 dentists. The data collection tools used were a socio-demographic form, the AIPS, and the Dental Profession Perceptions Scale (DPPS). Construct validity was assessed using exploratory and confirmatory factor analysis. Multivariate analysis of variance was utilized to test the difference between AIPS scores among groups. Results: The AIPS contained 26 items measured on a 5-point Likert response scale and demonstrated excellent internal and test-retest reliability. Exploratory and confirmatory factor analyses of the AIPS identified six factors that categorized perceptions of AI, including 'Human', 'Security', 'Accessibility', 'Vocational', 'Technology', and 'Cost'. The six-factor solution of the AIPS model demonstrated a good fit for the data. AIPS scores varied depending on gender, working place, occupational experience, the need to use AI, and the frequency of AI use in dental practice. The total AIPS score had the strongest correlation with the "human" factor and the weakest correlation with the "accessibility" factor. Statistically significant correlations were observed between the AIPS score and DPPS total, as well as each of its three sub-scales. Conclusions: This study developed a new scale, the AI Perceptions Scale (AIPS), to evaluate perceptions of AI in healthcare. The perceptions of dentists towards AI were categorized into six distinct factors. The AIPS scale was found to be a reliable and valid measurement tool, indicating that it can be effectively used in future research.

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