Artificial Intelligence for American Society of Anesthesiologists Physical Status Classification: Agreement with Clinician Consensus and Temporal Stability Analysis

Background: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used for perioperative communication and research with known variation in agreement amongst assessors. Large language models (LLM) are increasingly considered for uniform decision making due to agreement challenges within clinicians under identical inputs. The study compared four contemporary LLMs against clinician-derived consensus and quantified within-model stability across repeated assessments. Methods: In a cross-sectional vignette study, 228 anesthesiologists from Denmark, England, and India classified 20 standardized perioperative vignettes through online survey. The modal response per vignette was considered as clinician consensus. Four LLMs (ChatGPT-5.2 Plus, Gemini 3 Pro, Perplexity AI Pro, Claude 4 Sonnet) received same set of vignettes through identical prompts. Temporal stability was assessed by repeating each vignette query nine times per model (three-time windows across three nonconsecutive days) in fresh sessions. Primary outcome was exact agreement with clinician consensus. Results: Consensus agreement for modal LLM classifications was 18/20 (90%) for ChatGPT, 17/20 (85%) for Gemini, 17/20 (85%) for Claude, and 15/20 (75%) for Perplexity. Disagreement is clustered in vignettes with weak or split clinician consensus. Stability differed by model: Claude was fully stable across all vignettes (20/20), Gemini 19/20, ChatGPT 18/20, and Perplexity 14/20; instability typically involved adjacent-class shifts. Conclusions: Contemporary LLMs often match clinician modal judgement and are largely temporally stable, with discordance concentrated in clinically ambiguous boundary cases.