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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.

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Summary

Most occupational and environmental medicine clinicians do not use large language models (LLMs) but are interested in them. Trust is a key factor influencing their intention to adopt LLMs in practice.

Keywords:
artificial intelligenceclinicianshuman factorslarge language modelsoccupational and environmental medicinetechnology adoption

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Area of Science:

  • Occupational and Environmental Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Large Language Models (LLMs) are emerging AI tools with potential applications in medicine.
  • Understanding clinician perspectives is crucial for the adoption of new technologies in healthcare.

Purpose of the Study:

  • To investigate U.S. occupational and environmental medicine (OEM) clinicians' current use, knowledge, and interest in LLMs.
  • To identify factors influencing the adoption of LLMs in OEM practice.

Main Methods:

  • An online survey and semi-structured interviews were conducted with U.S. OEM clinicians.
  • Quantitative and qualitative data analyses were performed to assess perceptions and practices.

Main Results:

  • 70% of survey respondents (n=42) reported no current use of LLMs in clinical practice.
  • Composite Trust scores significantly predicted the intention to use LLMs (B = 0.57, p = 0.019).
  • Interview data corroborated survey findings, highlighting interest alongside current non-usage.

Conclusions:

  • Despite low current adoption, a majority of OEM clinicians expressed interest in using LLMs.
  • Clinician trust in LLMs is a significant predictor of their intention to integrate these tools into practice.