The Effectiveness of Using Artificial Intelligence (Chat GPT) in Cardiac Assessment During Anesthesia Examination of Preoperative Cases
The Effectiveness of Using Artificial Intelligence (Chat GPT) in Cardiac Assessment During Anesthesia Examination of Preoperative Cases
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status classification I-IV
Scheduled for non-cardiac surgery
Patients evaluated preoperatively by anesthesia residents with less than two years of clinical experience
Availability of complete clinical data including medical history, ECG findings, and chest radiography
Ability to provide informed consent
Exclusion Criteria:
Patients younger than 18 years of age
Patients undergoing cardiac surgery
Patients with incomplete clinical data
Patients who declined participation
Emergency surgery cases
Patients unable to undergo standard preoperative evaluation