CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation

TL;DR

CARE-ECG introduces a causally structured framework for ECG interpretation that enhances diagnostic accuracy, explanation faithfulness, and counterfactual reasoning by integrating representation learning, causal inference, and language grounding.

Contribution

It unifies ECG representation, diagnosis, and explanation into a single pipeline with causal reasoning and counterfactual assessment, improving interpretability and clinical utility.

Findings

Achieves 0.84 accuracy on Expert-ECG-QA benchmark.

Reduces hallucinations in language outputs.

Provides traceable causal explanations for ECG diagnoses.

Abstract

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. CARE-ECG encodes multi-lead ECGs into temporally organized latent biomarkers, performs causal graph inference for probabilistic diagnosis, and supports counterfactual assessment via structural causal models. To improve faithfulness, CARE-ECG grounds language outputs through causal retrieval-augmented generation and a modular agentic pipeline that integrates history, diagnosis, and response with verification. Across multiple ECG benchmarks and expert QA settings, CARE-ECG improves diagnostic accuracy and explanation faithfulness while reducing hallucinations (e.g., 0.84 accuracy on Expert-ECG-QA and 0.76 on SCP-mapped PTB-XL under GPT-4). Overall, CARE-ECG provides traceable reasoning by exposing key latent drivers, causal evidence paths, and how alternative physiological states would change outcomes.