Understanding AI Acceptance in Healthcare: A Mixed-Method Study on Trialability and Doctor-Patient Relationships

Abstract

The implementation of artificial intelligence is transforming healthcare practices by its ability to improve its practices. However, despite the benefits, its widespread acceptance remains a challenge. This study aims to advance the understanding of the key antecedents of AI acceptance by patients, using an extension of the TAM by incorporating trialability and doctor-patient relationships. A sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare and then a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The results confirmed that trialability significantly enhances AI adoption by lowering uncertainty, while strong doctor-patient relationships positively influence perceived usefulness. In addition, findings showed that Perceived usefulness is the strongest antecedent of AI adoption while perceived ease of use plays a secondary role. The study offers theoretical advancements in AI adoption models and practical insights by highlighting the need for healthcare providers and policymakers to emphasize AI trial opportunities and foster trust-based patient communication.

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