Comparison of Artificial Intelligence and Anesthesiologist in Preoperative Risk Assessment

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

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