CARE-ECG：基于因果代理的可解释与反事实ECG解读
CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation
AI总结：
CARE-ECG通过统一的表示学习、诊断和解释流程，提升ECG解读的准确性与可信度，减少幻觉，适用于多基准和专家问答场景。
摘要：
大型语言模型（LLMs）能够实现波形到文本的ECG解读和交互式临床提问，但大多数ECG-LLM系统仍依赖于弱信号-文本对齐和检索，缺乏显式的生理或因果结构。这限制了基础、时间推理和反事实
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
