ECG-LENS: AI Framework for Generating Clinical ECG Reports

Researchers have introduced ECG-LENS, an end-to-end framework for automated electrocardiogram (ECG) report generation, as detailed in a preprint on arXiv (arXiv:2608.05893v1). The system integrates multi-lead signal modeling, diagnosis-aware representations, and clinical context to produce coherent diagnostic reports from ECG recordings. This addresses the challenge of transforming multi-lead ECG data into reliable clinical text, a task that requires analyzing subtle temporal morphologies and expressing findings in dense clinical terminology. Unlike existing systems that focus primarily on classification, ECG-LENS aims to generate reports suitable for practical clinical use, potentially reducing clinicians' interpretive workload, improving diagnostic efficiency, and expanding access to cardiac assessment in underserved communities. The framework is designed to jointly model the ECG signals and the diagnostic reasoning process, producing outputs that are more accurate and clinically relevant than current methods. The preprint was announced on arXiv, with the identifier 2608.05893, and is available at the provided URL. The work represents a step forward in applying artificial intelligence to medical imaging and report generation, with implications for healthcare delivery and diagnostic accuracy.