Evaluation of the acceptance of artificial intelligence technologies in health with technology acceptance model

Abstract

PURPOSE: This study aimed to assess healthcare professionals' acceptance of artificial intelligence technologies within the framework of the Technology Acceptance Model. DESIGN/METHODOLOGY/APPROACH: Data were collected from 376 healthcare workers between May 21 and July 21, 2025, using a questionnaire. The study employed the 25-item, five-dimensional Technology Acceptance Scale. Reliability, normality, factor, descriptive, correlation, regression, and mediation analyses were performed using a quantitative research design. FINDINGS: The results of this study revealed that healthcare professionals exhibit a high level of acceptance of artificial intelligence technologies. It was found that perceived ease of use positively influences both perceived benefit and attitude; perceived benefit, in turn, positively affects attitude toward use and behavioral intention. Furthermore, attitude toward use was shown to positively influence intention and actual usage behavior. The results of the mediation analyses indicated that perceived benefit partially mediates the relationship between perceived ease of use and attitude, whereas attitude partially mediates the relationship between perceived benefit and behavioral intention. ORIGINALITY/VALUE: This study contributes contextual evidence regarding the acceptance of artificial intelligence technologies among healthcare professionals and provides managerial insights for the implementation of AI-supported healthcare systems. It also extends the Technology Acceptance Model by integrating real-world usage behaviors into the model and addressing the geographical and methodological limitations of previous research.

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