Agreement Between ChatGPT-5 and Anesthesiologists in Preoperative Risk Assessment: ASA Classification (ASA)

April 20, 2026 updated by: Damla Kaytancı Özçelik

Evaluating Large Language Models for Preoperative Risk Stratification: ChatGPT-5 vs. Anesthesiologists on ASA Classification and Blood Transfusion Prediction

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

Background and Rationale Preoperative risk assessment is a fundamental component of perioperative medicine and plays a central role in anesthetic planning, patient safety, and perioperative resource allocation. The American Society of Anesthesiologists Physical Status (ASA-PS) classification system remains the most widely used global method for describing preoperative health status. Despite its widespread adoption, ASA classification depends on clinician interpretation and may vary between evaluators.

Advances in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities for supporting clinical decision-making. These systems can process both structured and unstructured clinical information and may assist in standardizing certain medical classification tasks. In perioperative medicine, AI-assisted evaluation may help interpret patient comorbidities and clinical information in a consistent manner.

Another important component of preoperative assessment is laboratory test utilization. Preoperative laboratory testing is commonly used to identify potential perioperative risks; however, the number and type of tests ordered may vary among clinicians and institutions. AI-based systems may provide standardized recommendations for laboratory investigations and potentially contribute to more efficient resource utilization.

In addition, perioperative erythrocyte suspension (packed red blood cell, PRBC) transfusion represents an objective indicator of surgical physiological stress and perioperative resource use. Evaluating the relationship between risk classification and actual blood product utilization may help determine whether AI-assisted risk assessment has potential clinical relevance.

Study Design and Procedures This study is designed as a single-center observational study conducted at the preoperative anesthesia outpatient clinic of Antalya City Hospital. Adult patients undergoing routine preoperative anesthesia assessment before elective surgery during the study period will be included in the analysis.

For each patient, the ASA Physical Status classification assigned by the evaluating anesthesiologist during routine clinical care will be recorded. An anonymized summary of the same preoperative clinical information will then be analyzed by the artificial intelligence system (ChatGPT-5), which will generate an independent ASA classification.

The study will also compare laboratory test recommendations generated by the AI system with those ordered by anesthesiologists during routine preoperative evaluation. The number and types of laboratory tests recommended by each source will be recorded for comparison.

Information regarding perioperative erythrocyte suspension transfusion will be obtained from hospital electronic medical records. These data will be used to explore the relationship between risk classification and actual blood product utilization.

All clinical information used in the analysis will be anonymized before being processed by the AI system. AI outputs will not influence patient care or clinical decision-making.

Study Significance By comparing clinician-based and AI-generated preoperative assessments, this study aims to explore the potential role of large language models in supporting standardized risk stratification and resource utilization in anesthesia practice. The results may contribute to understanding whether AI-assisted evaluation can provide reliable support for preoperative clinical assessment.

Study Type

Observational

Enrollment (Actual)

703

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Adult patients (≥18 years) scheduled for elective surgery and evaluated at the preoperative anesthesia outpatient clinic of Antalya City Hospital during the study period will constitute the study population. All eligible patients with a completed standardized preoperative anesthesia assessment form and documented ASA Physical Status classification will be considered. Only patients whose clinical data can be fully anonymized and who provide written informed consent will be included.

Patients undergoing emergency surgery, pediatric patients, pregnant patients, ASA VI classification, incomplete documentation, or cases with more than 30 days between preoperative assessment and surgery will be excluded.

Description

Inclusion Criteria:

  • Age ≥ 18 years
  • Scheduled for elective surgery
  • Completed standardized preoperative anesthesia evaluation form
  • ASA Physical Status classification assigned by a specialist anesthesiologist
  • Written informed consent
  • Clinical documentation suitable for anonymization

Exclusion Criteria:

  • Emergency surgery
  • ASA VI classification
  • Pregnancy
  • Pediatric patients (<18 years)
  • Incomplete or non-standardized clinical documentation
  • Inability to anonymize clinical records
  • More than 30 days between preoperative assessment and surgery

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement Between Anesthesiologist-Assigned and ChatGPT-5-Generated ASA Physical Status Classification
Time Frame: At the time of preoperative anesthesia assessment (baseline).
Agreement between ASA Physical Status classifications assigned by board-certified anesthesiologists and those generated by ChatGPT-5 using anonymized preoperative clinical data. Agreement will be quantified using Cohen's kappa and weighted kappa statistics for ordinal ASA categories (I-V). The comparison will be performed using identical anonymized preoperative clinical summaries.
At the time of preoperative anesthesia assessment (baseline).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Damla Kaytancı Özçelik

Investigators

  • Principal Investigator: Damla Kaytancı Özçelik, Antalya City Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 10, 2026

Primary Completion (Actual)

February 10, 2026

Study Completion (Estimated)

May 1, 2026

Study Registration Dates

First Submitted

March 2, 2026

First Submitted That Met QC Criteria

March 5, 2026

First Posted (Actual)

March 9, 2026

Study Record Updates

Last Update Posted (Actual)

April 22, 2026

Last Update Submitted That Met QC Criteria

April 20, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Antalya City Hospital

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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