Comparison of Artificial Intelligence and Anesthesiologist in Preoperative Risk Assessment (AI-PREOP)
Clinical Performance of a Machine Learning-Based Artificial Intelligence System Compared With Anesthesiologist Assessment in Preoperative Patient Evaluation
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
Study Overview
Status
Conditions
Detailed Description
Preoperative evaluation is a critical component of perioperative care, aimed at identifying patient-specific risks, optimizing patient safety, and guiding anesthetic planning prior to elective surgical procedures. This process traditionally relies on the clinical judgment of anesthesiologists, who integrate medical history, physical examination findings, comorbid conditions, and relevant laboratory data to assess perioperative risk.
Recent advances in artificial intelligence and machine learning have enabled the development of clinical decision-support systems capable of analyzing complex clinical data and generating predictive risk assessments. Despite increasing interest in these technologies, their clinical performance and reliability in real-world preoperative settings remain insufficiently evaluated.
This observational study is designed to compare preoperative risk assessments generated by a machine learning-based artificial intelligence system with routine anesthesiologist-led evaluations. Adult patients scheduled for elective surgery will undergo standard preoperative assessment performed by anesthesiologists as part of usual clinical care. Independently, anonymized patient data will be processed by the artificial intelligence system to produce preoperative risk assessments. The artificial intelligence output will not be available to clinicians and will not influence patient management.
The primary outcome of the study is the level of agreement between the artificial intelligence system and anesthesiologists in preoperative risk stratification. Secondary outcomes include the consistency, concordance, and overall performance of artificial intelligence-generated assessments compared with clinician evaluations.
This study involves no interventions and does not alter standard patient care. All anesthetic and perioperative management decisions will remain entirely under the responsibility of the treating anesthesiologist. By systematically comparing artificial intelligence-based assessments with clinician evaluations, this study aims to clarify the potential role, strengths, and limitations of artificial intelligence as a supportive tool in routine preoperative evaluation
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Trabzon
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Trabzon, Trabzon, Turkey (Türkiye)
- Trabzon Faculty of Medicine, Kanuni Training and Research Hospital,
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- - Adult patients aged 18 years and older
- Patients scheduled for elective surgery under anesthesia
- Patients who underwent routine preoperative evaluation
- Availability of complete preoperative clinical data required for both anesthesiologist and artificial intelligence-based assessment
Exclusion Criteria:
- - Patients younger than 18 years
- Emergency surgery cases
- Patients with incomplete or missing preoperative clinical data
- Patients who declined participation or whose data could not be evaluated
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Rate of Agreement Between Artificial Intelligence-Based and Anesthesiologist Preoperative Risk Assessments
Time Frame: At the time of preoperative evaluation
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This outcome measures the level of agreement between an artificial intelligence-based preoperative evaluation system and anesthesiologist assessment, including American Society of Anesthesiologists (ASA) physical status classification and overall perioperative risk stratification.
Agreement will be evaluated using appropriate statistical measures.
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At the time of preoperative evaluation
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Gülgün E Aksoy, MD, Trabzon Faculty of Medicine, Kanuni Training and Research Hospital, Turkey
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- PREOP-AI-2025
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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