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Comparison of Artificial Intelligence and Anesthesiologist in Preoperative Risk Assessment

NCT07364942 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2026-01-23

No results posted yet for this study

Summary

Preoperative evaluation is essential for identifying patient-related risks before elective surgery and for planning safe anesthesia management. Traditionally, this evaluation is performed by anesthesiologists based on clinical history, physical examination, comorbidities, and laboratory findings.

This observational study aims to compare the clinical performance of a machine learning-based artificial intelligence system with anesthesiologist assessment during preoperative patient evaluation. The artificial intelligence system independently analyzes patient data and generates risk assessments, which are then compared with evaluations performed by anesthesiologists.

The primary objective of the study is to assess the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk assessment. Secondary objectives include evaluating the accuracy and consistency of the artificial intelligence system and exploring its potential role as a decision-support tool in preoperative clinical practice.

The findings of this study may contribute to understanding the potential benefits and limitations of artificial intelligence-assisted decision making in preoperative evaluation

Conditions

  • Preoperative Risk Assessment

Sponsors & Collaborators

  • Gülgün Elif Aksoy

    lead OTHER

Principal Investigators

  • Gülgün E Aksoy, MD · Trabzon Faculty of Medicine, Kanuni Training and Research Hospital, Turkey

Eligibility

Min Age
18 Years
Max Age
99 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-03-01
Primary Completion
2025-05-01
Completion
2025-10-30

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 NCT07364942 on ClinicalTrials.gov