ECAG2
A multi-agent AI framework for ECG interpretation across diverse cardiac scenarios.
Motivation
Modern ECG interpretation is rarely a single act of pattern recognition — it’s a layered reasoning process across rhythm, morphology, ischemia, and patient context that human cardiologists do almost effortlessly but that single end-to-end models still struggle to make transparent. ECAG2 explores whether a society of specialized AI agents, each focused on one diagnostic dimension, can collaborate to deliver explainable cardiac reasoning.
How it works
The framework decomposes ECG analysis into specialist agents — each responsible for a focused subdomain such as arrhythmia detection, morphological analysis, or scenario-specific pattern recognition — that share a common signal-processing backbone and coordinate through a structured tool layer. Together they reason over a curated library of ECG scenarios, producing both a diagnostic conclusion and a traceable account of how the conclusion was reached.