AI-Based ASA Classification in Preoperative Patients (AI-Based ASA C)

Evaluation of Artificial Intelligence Models in Assigning American Society of Anesthesiologists Physical Status Classification in Preoperative Patients: A Prospective Observational Study

This prospective observational study aims to evaluate the performance of multiple artificial intelligence-based large language models in assigning American Society of Anesthesiologists Physical Status (ASA-PS) classifications in adult preoperative patients. AI-generated ASA scores obtained using both prompted and unprompted clinical scenario inputs will be compared with assessments performed by experienced anesthesiologists. The agreement, accuracy, readability, and overall quality of AI outputs will be analyzed to determine the potential role of artificial intelligence in supporting preoperative risk stratification.

Study Overview

• Status: Completed
• Conditions: Preoperative Risk Assessment; ASA Physical Status Classification

Detailed Description

The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used for perioperative risk stratification but is subject to interobserver variability. Recent advances in artificial intelligence and large language models have introduced new opportunities for clinical decision support.

This prospective observational study includes adult patients undergoing routine preoperative anesthesia evaluation at Bursa City Hospital. Demographic data, medical history, comorbidities, functional capacity, laboratory findings, electrocardiography, chest imaging results, and planned surgical procedures are recorded to construct standardized clinical scenarios.

Multiple artificial intelligence models, including large language model-based systems, are provided with patient scenarios using both structured prompts and unstructured inputs. Each model assigns an ASA-PS classification and provides explanatory text. AI-generated classifications are compared with assessments performed independently by experienced anesthesiologists.

Primary outcomes include agreement and accuracy between AI-generated and clinician-assigned ASA classifications using Cohen's Kappa statistics. Secondary outcomes include readability assessment using the Ateşman Turkish Readability Index and response quality evaluation using the Global Quality Scale.

The study aims to explore whether artificial intelligence can improve standardization, objectivity, and efficiency in preoperative risk assessment while highlighting the strengths and limitations of current AI technologies in clinical anesthesia practice.

• Study Type: Observational
• Enrollment (Actual): 128

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Nilüfer
    • Bursa, Nilüfer, Turkey (Türkiye), 16110
      • Bursa City Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

The study population consists of adult patients presenting for routine preoperative anesthesia assessment at Bursa City Hospital, including individuals with varying comorbidities and surgical risk profiles.

Description

Inclusion Criteria:

• Adult patients aged 18 years or older
• Undergoing routine preoperative anesthesia evaluation
• Classified as ASA Physical Status I-IV
• Availability of complete clinical data required for AI assessment

Exclusion Criteria:

• Patients younger than 18 years
• Refusal to participate
• Incomplete or missing clinical information

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement Between AI-Generated and Clinician-Assigned ASA Physical Status Classification	Level of agreement between artificial intelligence models and anesthesiologists in assigning ASA Physical Status classification measured using Cohen's Kappa coefficient	Preprocedural/Perioperative

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of AI Models in ASA Classification	Proportion of correct ASA Physical Status classifications generated by artificial intelligence models compared with anesthesiologist assessments	Preprocedural/Perioperative
Readability of AI-Generated Clinical Responses	Readability scores of artificial intelligence-generated clinical responses assessed using the Ateşman Turkish Readability Index (range: 0-100), where higher scores indicate better readability.	Preprocedural/Perioperative

Collaborators and Investigators

• Sponsor: Bursa City Hospital

Study record dates

Study Major Dates

• Study Start (Actual): December 15, 2024
• Primary Completion (Actual): December 15, 2024
• Study Completion (Actual): January 15, 2026

Study Registration Dates

• First Submitted: February 4, 2026
• First Submitted That Met QC Criteria: February 10, 2026
• First Posted (Actual): February 12, 2026

Study Record Updates

• Last Update Posted (Actual): June 17, 2026
• Last Update Submitted That Met QC Criteria: June 16, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Preoperative Evaluation; Large Language Models; ASA Classification
• Other Study ID Numbers: Bursa City Hospital 004

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 institutional data protection policies.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No