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AI Assisted Preoperative Assessment for ASA Classification and ICU Admission

NCT07783334 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 2500

Last updated 2026-08-24

No results posted yet for this study

Summary

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.

Conditions

  • Preoperative Anesthesia Assesment

Interventions

OTHER

AI-assisted preoperative assesment

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.

Sponsors & Collaborators

  • Bakirkoy Dr. Sadi Konuk Research and Training Hospital

    lead OTHER_GOV

Principal Investigators

  • evrim kucur tülübaş, MD · Bakirkoy Dr. Sadi Konuk Research and Training Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-02-16
Primary Completion
2026-08-20
Completion
2026-08-20

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07783334 on ClinicalTrials.gov