Accurate preoperative risk stratification is essential for perioperative planning, resource allocation, and patient safety. The American Society of Anesthesiologists Physical Status (ASA-PS) classification remains the most widely used global system for assessing preoperative health status. However, ASA classification relies on clinician judgment and may demonstrate inter-observer variability. Recent advances in artificial intelligence (AI), particularly large language models (LLMs), have shown potential for assisting clinical decision-making by synthesizing structured and unstructured medical information. In perioperative medicine, AI systems may support more standardized risk assessment and laboratory testing strategies. The objective of this observational study is to evaluate the agreement between ASA classifications assigned by anesthesiologists and those generated by a large language model (ChatGPT-5) using anonymized preoperative clinical information. The study will also examine differences in laboratory test recommendations and explore the relationship between clinician- and AI-generated risk assessments and perioperative erythrocyte suspension utilization. Adult patients scheduled for elective surgery who undergo routine preoperative anesthesia assessment will be included. For each patient, the ASA classification assigned by the anesthesiologist will be recorded and compared with the classification generated by the AI system using the same anonymized clinical information. This study aims to assess whether AI-assisted preoperative evaluation may support more consistent risk stratification and potentially contribute to more standardized perioperative resource utilization.
Age range
18 Years
Sex
ALL
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Agreement Between Anesthesiologist-Assigned and ChatGPT-5-Generated ASA Physical Status Classification
Timeframe: At the time of preoperative anesthesia assessment (baseline).