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US Occupational Medicine Clinicians' Perceptions and Practices With Respect to Artificial Intelligence Large Language
Zaira S Chaudhry1, Avishek Choudhury
1From the Industrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, West Virginia.
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.
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