US Occupational Medicine Clinicians' Perceptions and Practices With Respect to Artificial Intelligence Large Language Models: A Mixed-Methods Investigation.

Journal: Journal of occupational and environmental medicine
Published Date: Nov 13, 2025

Abstract

OBJECTIVE: The aim of the study was to explore US occupational and environmental medicine (OEM) clinicians' perceptions, knowledge, practices, and interest surrounding large language models (LLMs). METHODS: An online survey and semistructured interviews were conducted between April 2024 and July 2025 with a sample of US OEM clinicians. Quantitative and qualitative data analyses were performed. RESULTS: There were 60 survey respondents and 10 interviewees. Most respondents reported that they do not currently use LLMs in their clinical practice (70.0%, n = 42). Composite trust scores significantly predicted intention to use LLMs ( B = 0.57, P = 0.019, 95% CI [0.10, 1.03]). The interview data converged with and complemented the survey findings. CONCLUSIONS: Although most OEM clinicians in this sample reported not using LLMs in clinical practice, the majority expressed an interest, with trust being a significant predictor of intention to use LLMs.

Authors

  • Zaira S Chaudhry
    From the Industrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia (Z.S.C, A.C.).
  • Avishek Choudhury
    School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States.

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