Explicit Frailty Integration Reveals Cognitive Differences in ASA Classification Between Anesthesiologists and Large Language Models

The American Society of Anesthesiologists (ASA) Physical Status Classification System is widely used to assess perioperative risk, but it does not explicitly include frailty as a standardized variable. In daily clinical practice, anesthesiologists may implicitly incorporate frailty-related information into ASA classification based on individual clinical judgment, which may lead to variability between evaluators. In recent years, large language models (LLMs), a type of artificial intelligence, have been increasingly used in medical decision-support research. Unlike human clinicians, these models process information in a structured and explicit manner, without relying on intuition or implicit reasoning. The primary objective of this study is to compare ASA Physical Status classifications assigned by anesthesiologists and by two different large language models using standardized preoperative clinical data from adult patients undergoing elective surgery. A secondary objective is to evaluate how the addition of a frailty index influences ASA classification decisions made by human experts and artificial intelligence models. This prospective observational study aims to improve understanding of differences in clinical reasoning between anesthesiologists and artificial intelligence systems and to explore the role of frailty in perioperative risk assessment.