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

This study is designed as a prospective, observational, comparative, multirater investigation evaluating differences in ASA Physical Status Classification between anesthesiologists and large language models (LLMs).

The ASA Physical Status Classification System is a cornerstone of perioperative risk assessment; however, it lacks explicit incorporation of frailty, a multidimensional concept reflecting reduced physiological reserve and vulnerability. In clinical practice, anesthesiologists often integrate frailty-related information implicitly into ASA assessments, potentially contributing to interobserver variability.

Large language models process clinical information using explicit, structured inputs and do not rely on experiential or intuitive reasoning. This characteristic provides a unique opportunity to explore cognitive differences between human experts and artificial intelligence in clinical classification tasks.

Adult patients (≥18 years) scheduled for elective surgery will be included. Emergency cases, pediatric patients, and individuals with insufficient clinical data to allow ASA classification will be excluded. For each patient, standardized preoperative clinical data will be collected, including demographic characteristics, body mass index, comorbidities, regular medications, and type of planned surgical procedure.

ASA Physical Status Classification will be independently assigned by four board-certified anesthesiologists with at least five years of clinical experience, as well as by two large language models developed by different organizations. All evaluations will be conducted using the same standardized dataset, and evaluators will be blinded to each other's assessments.

The study will be conducted in two sequential phases. In the first phase, ASA classification will be performed using standard clinical data alone. In the second phase, a validated frailty index will be added to the same patient dataset, and the evaluation process will be repeated. This design will allow assessment of how frailty information affects ASA classification decisions in human and artificial intelligence evaluators.

Large language models will be prompted using a predefined, standardized prompt that remains unchanged throughout the study. Models will be instructed to generate a single ASA Physical Status category (I-V) without providing explanations or additional commentary, and no iterative prompting or feedback will be allowed.

Interrater agreement among anesthesiologists, between artificial intelligence models, and between human and artificial intelligence evaluators will be analyzed using Cohen's Kappa and Fleiss' Kappa statistics, as appropriate. Changes in ASA classification following the addition of frailty information will be evaluated using paired statistical methods. Statistical significance will be defined as p < 0.05.

By comparing ASA classification patterns between anesthesiologists and large language models, both with and without frailty data, this study aims to clarify the role of implicit and explicit reasoning in perioperative risk assessment and to contribute to the development of future artificial intelligence-assisted clinical decision-support systems.