AI Assisted Preoperative Assessment for ASA Classification and ICU Admission

August 20, 2026 updated by: Zeynep Lee, Bakirkoy Dr. Sadi Konuk Research and Training Hospital

AI Assisted Clinical Decision-making in Preoperative Anesthesia Assessment: a Comparision of Clinicians, ChatGPT and Gemini

This prospective observational study evaluates the performance of artificial intelligence (AI) models in preoperative anesthesia assessment. Preoperative clinical data from adult patients undergoing elective surgery are independently evaluated by clinicians and AI models (ChatGPT and Gemini). The study compares their assessments of American Society of Anesthesiologists (ASA) physical status classification and the predicted need for intensive care unit (ICU) admission within the first 24 hours after surgery. Actual postoperative ICU admission is used as the clinical outcome for evaluating predictive performance. No treatment or clinical decision is determined by the AI models, and patient management is performed according to routine clinical practice.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

2500

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

    • Istanbul
      • Istanbul, Istanbul, Turkey (Türkiye), 34147
        • Bakırkoy Dr. Sadi Konuk Training and Research Hospital

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

Yes

Sampling Method

Non-Probability Sample

Study Population

Adult patients undergoing elective surgery who are evaluated in the preoperative anesthesia clinic at Bakirkoy Dr. Sadi Konuk Research and Training Hospital.

Description

Inclusion Criteria:

  • Age 18 years or older
  • Scheduled for elective surgery
  • Evaluated in the preoperative anesthesia clinic
  • Provision of informed consent

Exclusion Criteria:

  • Pregnancy
  • Presence of an upper respiratory tract infection
  • Active herpes infection or active wound and/or lesion in the anesthesia-related area
  • Age under 18 years
  • Emergency surgery
  • Planned cardiovascular surgery
  • Incomplete clinical data

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
elective surgical patients
Adult patients undergoing elective surgery who undergo routine preoperative anesthesia assessment. Preoperative clinical data from each participant are independently evaluated by the clinician and the AI models ChatGPT and Gemini for ASA physical status classification and prediction of ICU admission within the first 24 postoperative hours. AI assessments do not influence clinical management or patient care.
Preoperative clinical data are independently evaluated using ChatGPT and Gemini for ASA physical status classification and prediction of ICU admission within 24 hours after surgery. AI-generated assessments are used for research purposes only and do not influence clinical decision-making or patient care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement in ASA Physical Status Classification
Time Frame: During preoperative assessment
Agreement between clinician-assigned and AI-generated ASA Physical Status classifications will be evaluated for ChatGPT and Gemini using linear weighted kappa statistics.
During preoperative assessment

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Prediction of Postoperative ICU Admission
Time Frame: Within the first 24 hours after surgery
The accuracy of preoperative predictions of postoperative ICU admission made by the clinician, ChatGPT, and Gemini will be evaluated against actual ICU admission occurring within the first 24 hours after surgery. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy will be calculated for each evaluator.
Within the first 24 hours after surgery

Collaborators and Investigators

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

Sponsor

Investigators

  • Study Director: evrim kucur tülübaş, MD, Bakirkoy Dr. Sadi Konuk Research and Training 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)

February 16, 2026

Primary Completion (Estimated)

August 20, 2026

Study Completion (Estimated)

August 20, 2026

Study Registration Dates

First Submitted

August 20, 2026

First Submitted That Met QC Criteria

August 20, 2026

First Posted (Actual)

August 24, 2026

Study Record Updates

Last Update Posted (Actual)

August 24, 2026

Last Update Submitted That Met QC Criteria

August 20, 2026

Last Verified

August 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • 2026/51

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data will not be shared due to patient confidentiality and data protection considerations.

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.