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Large Language Models Versus Anesthesiologists for ASA Physical Status Classification

NCT07696221 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 350

Last updated 2026-07-10

No results posted yet for this study

Summary

The American Society of Anesthesiologists Physical Status (ASA-PS) classification is a cornerstone of preoperative risk assessment, yet interrater variability among clinicians is well documented. Large language models (LLMs) have recently demonstrated expert-level performance in several clinical classification tasks, including ASA-PS assignment.

This retrospective observational study evaluates whether four widely used LLMs - ChatGPT, DeepSeek, Gemini, and Claude - can accurately and consistently assign ASA-PS classes from structured, fully anonymized clinical vignettes derived from real preoperative anesthesia evaluations, using a consensus of senior anesthesiologists as the reference standard.

No patient data will be transmitted to third-party platforms. Clinical information will be converted by the investigators into de-identified structured vignettes containing only age range, sex, body mass index range, presence or absence of systemic diseases, functional capacity, and the major/minor nature of the planned surgery, in full compliance with national data protection legislation (KVKK).

Conditions

  • Anesthesia
  • Preoperative Risk Prediction
  • Preoperative Risk Assessment

Sponsors & Collaborators

  • Marmara University Pendik Training and Research Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-21
Primary Completion
2026-08-21
Completion
2026-10-21

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07696221 on ClinicalTrials.gov