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How large language models can augment perioperative medicine: a daring discourse
  1. Rodney A Gabriel1,
  2. Edward R Mariano2,3,
  3. Julian McAuley4 and
  4. Christopher L Wu5
  1. 1Anesthesiology, University of California San Diego, La Jolla, California, USA
  2. 2Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, Palo Alto, California, USA
  3. 3Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
  4. 4Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
  5. 5Anesthesiology, Hospital for Special Surgery, New York City, New York, USA
  1. Correspondence to Dr Rodney A Gabriel, Anesthesiology, University of California San Diego, La Jolla, California, USA; ragabriel{at}


Interest in natural language processing, specifically large language models, for clinical applications has exploded in a matter of several months since the introduction of ChatGPT. Large language models are powerful and impressive. It is important that we understand the strengths and limitations of this rapidly evolving technology so that we can brainstorm its future potential in perioperative medicine. In this daring discourse, we discuss the issues with these large language models and how we should proactively think about how to leverage these models into practice to improve patient care, rather than worry that it may take over clinical decision-making. We review three potential major areas in which it may be used to benefit perioperative medicine: (1) clinical decision support and surveillance tools, (2) improved aggregation and analysis of research data related to large retrospective studies and application in predictive modeling, and (3) optimized documentation for quality measurement, monitoring and billing compliance. These large language models are here to stay and, as perioperative providers, we can either adapt to this technology or be curtailed by those who learn to use it well.

  • Analgesics, Opioid
  • Acute Pain

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  • Contributors All the authors were involved with concept design and preparation of initial and final manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests RG’s institution has received funding and/or product for research purposes from Epimed, Infutronix, SPR Therapeutics, Merck, and Precision Genetics. RG is a consultant for Avanos. ERM, JM, and CLW have no financial conflicts of interest to disclose.

  • Provenance and peer review Not commissioned; externally peer reviewed.