AI RESEARCH
CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation
arXiv CS.LG
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ArXi:2604.10420v1 Announce Type: new Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and retrieval without explicit physiological or causal structure. This limits grounding, temporal reasoning, and counterfactual "what-if" analysis central to clinical decision-making. We propose CARE-ECG, a causally structured ECG-language reasoning framework that unifies representation learning, diagnosis, and explanation in a single pipeline.