Technology meets trust: a UTAUT-driven exploration of AI-based medical device implementation in healthcare

Abstract

PURPOSE: Healthcare sectors are increasingly adopting artificial intelligence (AI) to speed up disease diagnosis, tailor treatments to individual patients, streamline routine processes, and improve overall patient safety in hospitals. Despite its great potential, there remains resistance among healthcare professionals towards AI adoption. The current study has identified the determinants influencing intentions to adopt AI-based medical devices among healthcare professionals. DESIGN/METHODOLOGY/APPROACH: The data were collected from various healthcare professionals using a structured questionnaire. The collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). FINDINGS: The findings indicate that performance expectancy, facilitating conditions, effort expectancy, and social influence have a significant influence on trust. Further, facilitating conditions, effort expectancy, and performance expectancy positively influence adoption intention. Again, trust holds substantial positive implications for the intention to adopt AI-based medical devices. Further, social influence shows an adverse effect. It does not provide a significant contribution towards adoption intention, but when the mediating relationship is tested, all the mediating relationships are supported, including social influence and adoption intention. PRACTICAL IMPLICATIONS: The findings offer valuable insights for healthcare professionals to introduce innovative medical devices that support the integration of AI-based medical technologies, thereby enhancing service efficiency and quality. ORIGINALITY/VALUE: This study makes a valuable contribution to the current body of literature pertaining to technology adoption with a focus on AI adoption among healthcare professionals.

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