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Physician Use of Large Language Models: A Quantitative Study Based on Large-Scale Query-Level Data.

Lin Qiu1, Chuang Tang2, Xuan Bi3

  • 1Department of Information Systems and Management Engineering, Southern University of Science and Technology, CoE North 907, 1088 Xueyuan Ave, Shenzhen, 518055, China, 86 0755 88012425.

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Summary
This summary is machine-generated.

Physicians primarily use generative artificial intelligence (GenAI) for medical research, with usage varying by gender, age, and device. Patient privacy risks appear low despite some sensitive information in queries.

Keywords:
artificial intelligencegenerative AIgenerative AI usagegenerative artificial intelligencehealth carelarge language modelsprivacy

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Area of Science:

  • Healthcare technology
  • Medical informatics
  • Artificial intelligence in medicine

Background:

  • Generative artificial intelligence (GenAI) is increasingly adopted in healthcare, but its real-world physician usage remains understudied.
  • Existing research focuses on theoretical applications, lacking insight into actual physician engagement with GenAI tools in practice.

Purpose of the Study:

  • To quantitatively analyze physician usage patterns of GenAI in clinical and research workflows.
  • To examine temporal trends and demographic variations in GenAI adoption.
  • To assess potential patient privacy risks associated with physician GenAI interactions.

Main Methods:

  • Collected and analyzed 106,942 query-and-answer pairs from 989 physicians over an 8-month period.
  • Employed topic classification to identify prevalent use cases and their evolution.
  • Developed sensitivity classifiers to detect personally identifiable information in queries to evaluate privacy risks.

Main Results:

  • Physicians predominantly use GenAI for medical research (60.2%) over clinical practice (12.25%).
  • Healthcare-related queries increased over time, particularly within the initial usage sequence.
  • Significant demographic variations observed: female and younger physicians used GenAI more for clinical/administrative tasks, while computer access correlated with research focus.

Conclusions:

  • Physicians are integrating GenAI, mainly for research but also for clinical support, with diverse usage across demographics.
  • While sensitive information appeared in queries, the overall risk of privacy breaches seems minimal.
  • GenAI adoption patterns highlight the need for tailored strategies considering physician demographics and workflow integration.