Large Language Models Versus Anesthesiologists for ASA Physical Status Classification

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Conditions studied: Anesthesia, Preoperative Risk Prediction, Preoperative Risk Assessment

In brief

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).

Key facts

Study ID
NCT07696221
Run by
Marmara University Pendik Training and Research Hospital
People needed
350
Starts
2026-07-21
Expected to finish
2026-10-21
Last updated by the study team
2026-07-10

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

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Full record on ClinicalTrials.gov

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