Large Language Models Versus Anesthesiologists for ASA Physical Status Classification

Plain-language 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).

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