A Technology Acceptance Model Survey of Using Large Language Models (LLMs) in Healthcare and Medical Practice

Abstract

As Large Language Models (LLMs) like ChatGPT become increasingly integrated into healthcare, understanding the factors that influence their acceptance by medical professionals is critical. This study, which is the first of its kind, applies an extended Technology Acceptance Model (TAM) to investigate behavioral intention to use LLMs among healthcare professionals. A cross-sectional survey (n = 426) was conducted, measuring Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Use (ATU), Trust, and Future Outlook, along with demographic variables such as age, gender, and professional role. Ordinary Least Squares (OLS) regression revealed that ATU was the strongest predictor of behavioral intention (𝑝 < 0.001), followed by Trust (𝑝 < 0.001) and PU (𝑝 = 0.001). PEOU was also significant (𝑝 = 0.012), while Future Outlook was not. Among demographic variables, age group (18-25 and 26-35), male gender, and the role of medical technician were significantly associated with higher intention to use LLMs. These findings highlight the central role of attitude, perceived value, and trust in LLM adoption, while also identifying demographic gaps that must be addressed to ensure equitable implementation. The study offers evidence-based guidance for designing training programs and policy frameworks that support responsible, inclusive deployment of LLMs in healthcare.