Public articles linked to the same research event.
Heart & Lung This study retrospectively analyzed 615 ECGs from 270 emergency medical service patient encounters with concern for acute myocardial infarction, using cardiology activation of the STEMI pathway as the reference standard, and compared three multimodal large language models with an ECG machine algorithm, finding that Gemini had the highest sensitivity (95.3%) but extremely poor specificity (9.4%), ChatGPT and Claude showed moderate sensitivity (68.1% and 67.2%) with limited specificity (42.3% and 46.5%), while the ECG machine algorithm was more balanced with sensitivity of 67.7% and specificity of 64.2%, suggesting that general-purpose large language models are not appropriate for ECG-based catheterization laboratory activation decisions in time-sensitive emergency workflows.
This study retrospectively analyzed 615 ECGs from 270 emergency medical service patient encounters with concern for acute myocardial infarction, using cardiology activation of the STEMI pathway as the reference standard, and compared three multimodal large language models with an ECG machine algorithm, finding that Gemini had the highest sensitivity (95.3%) but extremely poor specificity (9.4%), ChatGPT and Claude showed moderate sensitivity (68.1% and 67.2%) with limited specificity (42.3% and 46.5%), while the ECG machine algorithm was more balanced with sensitivity of 67.7% and specificity of 64.2%, suggesting that general-purpose large language models are not appropriate for ECG-based catheterization laboratory activation decisions in time-sensitive emergency workflows.
This study retrospectively analyzed 615 ECGs from 270 emergency medical service patient encounters with concern for acute myocardial infarction, using cardiology activation of the STEMI pathway as the reference standard, and compared three multimodal large language models with an ECG machine algorithm, finding that Gemini had the highest sensitivity (95.3%) but extremely poor specificity (9.4%), ChatGPT and Claude showed moderate sensitivity (68.1% and 67.2%) with limited specificity (42.3% and 46.5%), while the ECG machine algorithm was more balanced with sensitivity of 67.7% and specificity of 64.2%, suggesting that general-purpose large language models are not appropriate for ECG-based catheterization laboratory activation decisions in time-sensitive emergency workflows.
This study retrospectively analyzed 615 ECGs from 270 emergency medical service patient encounters with concern for acute myocardial infarction, using cardiology activation of the STEMI pathway as the reference standard, and compared three multimodal large language models with an ECG machine algorithm, finding that Gemini had the highest sensitivity (95.3%) but extremely poor specificity (9.4%), ChatGPT and Claude showed moderate sensitivity (68.1% and 67.2%) with limited specificity (42.3% and 46.5%), while the ECG machine algorithm was more balanced with sensitivity of 67.7% and specificity of 64.2%, suggesting that general-purpose large language models are not appropriate for ECG-based catheterization laboratory activation decisions in time-sensitive emergency workflows.