TL;DR
The clinician outperformed the LLMs in pediatric ECG interpretation and diagnostic accuracy varied markedly across the models, with reliability limits particularly evident in high-risk rhythms.
Findings suggest that AI models can extract complex information from ECG recordings beyond individual conventional parameters, which supports its role as a tool for automated ECG interpretation, disease detection, risk stratification, and indirect assessment of cardiac structure and function.
INTRODUCTION Artificial intelligence electrocardiogram (AI-ECG) interpretation has emerged as a promising approach to identify Brugada syndrome (BrS). This review sought to investigate diagnostic viability and clinical applicability of AI-ECG interpretation models for detecting BrS. METHODS A systematic search (PubMe...
For HF/LVSD, AI-ECG shows consistent and reproducible accuracy against echocardiography, and this estimate proved robust to the exclusion of studies at high risk of bias.
INTRODUCTION Vision-language models (VLMs) are increasingly explored for clinical interpretation tasks. Electrocardiographic (ECG) identification of acute coronary occlusion (ACO) is a high-risk application requiring accurate pattern recognition, and medical VLM performance on this task remains poorly characterised out...
BACKGROUND Normative pediatric electrocardiographic (ECG) parameters have been established in prior studies, but lack fine temporal resolution during early life. Accurate age-specific values are critical for diagnosis, risk stratification, and management of cardiovascular diseases (CVD). OBJECTIVE To derive high-temp...
BACKGROUND Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely identification of ST-elevation myocardial infarction (STEMI) and activation of reperfusion pathways in emergency care. OBJECTIVES To evaluate the diagnostic performance of multimodal LLMs in identifying prehospital ECGs warra...
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