CLAIMS: Clinical Labeling and Abnormality Inference from Multilead ECG using LLMs with Evidence Citation
CLAIMS is an explainable clinical ECG interpretation framework combining rule-based signal features, CNN embeddings, and LLM-guided narrative synthesis. Designed for the PTB-XL dataset, it generates transparent diagnostic reports that include evidence citations, improving trustworthiness and interpretability over traditional black-box deep learning models.