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This paper is in the following e-collection/theme issue:

Artificial Intelligence (5110) Ontologies, Classifications, and Coding (479) Machine Learning (3381) New and Emerging Methods and Tools in Surgery and Anesthesiology (77) Perioperative Risk Assessment (61) Artificial Intelligence in Surgery and Perioperative Medicine (99) Clinical Decision Support for Anesthesiology (31)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/90724, first published .
Patient and nurse discuss medical data on laptop in modern hospital room

American Society of Anesthesiologists Physical Status Classification: Hybrid Machine Learning–Large Language Model Ensemble Retrospective Validation Study

American Society of Anesthesiologists Physical Status Classification: Hybrid Machine Learning–Large Language Model Ensemble Retrospective Validation Study

Authors of this article:

Christabelle Pabalan1 Author Orcid Image ;   Alvin Stewart2 Author Orcid Image ;   Gabrielle Fisher1, 3 Author Orcid Image ;   Andrew Fisher1, 3 Author Orcid Image
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Christabelle Pabalan   1 , MS ;   Alvin Stewart   2 , MD ;   Gabrielle Fisher   1, 3 , MD ;   Andrew Fisher   1, 3 , MD

1 Qventus Inc, Mountain View, CA, United States

2 Department of Anesthesiology, University of Arkansas Medical Center, Little Rock, AR, United States

3 Department Anesthesiology and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, United States

Corresponding Author:

  • Andrew Fisher, MD
  • Department Anesthesiology and Perioperative Medicine
  • Medical University of South Carolina
  • 135 Rutledge Ave
  • Charleston, SC 29425
  • United States
  • Phone: +1 (843) 792-2322
  • Email: fisheran@musc.edu