How large language models can augment perioperative medicine: a daring discourse.

Acute Pain Analgesics, Opioid CHRONIC PAIN TECHNOLOGY

Journal

Regional anesthesia and pain medicine
ISSN: 1532-8651
Titre abrégé: Reg Anesth Pain Med
Pays: England
ID NLM: 9804508

Informations de publication

Date de publication:
11 2023
Historique:
received: 27 04 2023
accepted: 07 06 2023
medline: 25 9 2023
pubmed: 20 6 2023
entrez: 19 6 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 37336616
pii: rapm-2023-104637
doi: 10.1136/rapm-2023-104637
doi:

Types de publication

Review Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

575-577

Informations de copyright

© American Society of Regional Anesthesia & Pain Medicine 2023. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

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.

Auteurs

Rodney A Gabriel (RA)

Anesthesiology, University of California San Diego, La Jolla, California, USA ragabriel@health.ucsd.edu.

Edward R Mariano (ER)

Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, Palo Alto, California, USA.
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA.

Julian McAuley (J)

Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.

Christopher L Wu (CL)

Anesthesiology, Hospital for Special Surgery, New York City, New York, USA.

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