Frailty Assessment Reveals Cognitive Differences in ASA Classification: Anesthesiologists vs Large Language Models (ASA-AI)

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.

Study Overview

• Status: Completed
• Conditions: Perioperative Risk Assessment
• Intervention / Treatment: Other: Observational Assessment Only

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 200

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Istanbul, Turkey (Türkiye)
    • Istanbul Provincial Health Directorate Fatih Sultan Mehmet Training and Research Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Adult patients undergoing elective surgical procedures whose standardized preoperative clinical data are evaluated for ASA Physical Status Classification by anesthesiologists and large language models.

Description

Inclusion Criteria

• Adult patients aged 18 years and older
• Scheduled for elective surgical procedures
• Availability of standardized preoperative clinical data sufficient for ASA Physical -Status Classification Exclusion Criteria
• Emergency surgical procedures
• Pediatric patients
• Patients with insufficient or incomplete clinical data preventing ASA Physical --Status assessment

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Adult Elective Surgery Patients; Adult patients aged 18 years and older scheduled for elective surgical procedures. Standardized preoperative clinical data will be evaluated by anesthesiologists and large language models for ASA Physical Status Classification, with and without the addition of frailty information.	Other: Observational Assessment Only; This is an observational study with no clinical intervention. No treatment, procedure, drug, or device is assigned as part of the study. ASA Physical Status Classification is assessed using existing preoperative clinical data.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Interrater Agreement in ASA Physical Status Classification Between Anesthesiologists and Large Language Models	Agreement in ASA Physical Status Classification (ASA I-V) between four anesthesiologists and two large language models based on standardized preoperative clinical data, assessed using interrater agreement statistics.	At the time of preoperative evaluation, prior to surgery

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Effect of Frailty Information on ASA Physical Status Classification	Change in ASA Physical Status Classification assigned by anesthesiologists and large language models after the addition of a frailty index to standardized preoperative clinical data.	At the time of preoperative evaluation, prior to surgery
Agreement Between Large Language Models in ASA Physical Status Classification	Interrater agreement in ASA Physical Status Classification between two different large language models using identical standardized preoperative clinical datasets, with and without frailty information.	At the time of preoperative evaluation, prior to surgery
Agreement Among Anesthesiologists in ASA Physical Status Classification	Interrater agreement in ASA Physical Status Classification among four board-certified anesthesiologists based on standardized preoperative clinical data, with and without frailty information.	At the time of preoperative evaluation, prior to surgery

Collaborators and Investigators

• Sponsor: Fatih Sultan Mehmet Training and Research Hospital
• Investigators: Principal Investigator: cansu ofluoglu, md, Istanbul Provincial Health Directorate Fatih Sultan Mehmet Training and Research Hospital

Study record dates

Study Major Dates

• Study Start (Actual): February 25, 2026
• Primary Completion (Actual): April 1, 2026
• Study Completion (Actual): April 1, 2026

Study Registration Dates

• First Submitted: January 29, 2026
• First Submitted That Met QC Criteria: February 3, 2026
• First Posted (Actual): February 10, 2026

Study Record Updates

• Last Update Posted (Actual): April 23, 2026
• Last Update Submitted That Met QC Criteria: April 20, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Clinical Decision Support; Artificial Intelligence; Frailty; Anesthesiology; Preoperative Assessment; Large Language Models; ASA Physical Status Classification; Interrater Agreement; Kappa Statistics
• Additional Relevant MeSH Terms: Pathologic Processes; Pathological Conditions, Signs and Symptoms; Frailty
• Other Study ID Numbers: AI_ASA_FRAILTY_2026

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No