🤖 AI Summary
This study addresses the limitation of existing arrhythmia analysis methods, which typically identify only rhythm types without providing personalized clinical decision support. The authors propose the first end-to-end multi-agent system that integrates single-lead electrocardiogram and photoplethysmography signals to construct patient-level rhythm profiles. By incorporating clinical data and retrieval-augmented guideline reasoning, the system generates auditable diagnostic and therapeutic recommendations. It uniquely unifies rhythm assessment, clinical significance interpretation, and urgency-based management advice within a collaborative multi-agent framework, further validated through expert alignment mechanisms. The approach achieves state-of-the-art performance across tasks including comprehensive diagnosis, clinical relevance determination, urgency stratification, and management suggestion, with inter-rater agreement (ICC = 0.74 with cardiologists and 0.66 with large language models) comparable to inter-expert consensus (ICC = 0.67).
📝 Abstract
The same episode of atrial fibrillation is a minor finding in a healthy adult and grounds for anticoagulation in an elderly patient with hypertension: identical signal, opposite decision. Naming the rhythm is only the start; what determines a patient's outcome is the judgement that follows -- what the arrhythmia is across the whole record, what it means for this patient, and what should be done about it. Recent work pairing large language models with the ECG stops short of this, reading one recording without assembling a patient-level finding; and agentic systems built around it either receive the arrhythmia a device has already detected or target a different diagnostic task, stopping before the decision this task requires. We formulate patient-level arrhythmia decision support as a task and present Cardiologent, a multi-agent system that spans it from detection to decision. An agent for each signal -- a single ECG lead and the photoplethysmogram a wearable acquires -- grounds its window reading in measured features rather than a bare label; the readings are assembled into the patient's rhythm profile and, with the patient's own data, reasoned against clinical guidelines retrieved for the case, with a critic checking each conclusion against the guideline it cites. We evaluate the clinical decision rather than the report, across integrated diagnosis, clinical significance, and urgency and management. Cardiologent scores highest on every axis, first on every patient-level task under both cardiologists and an at-scale LLM judge -- whose agreement with the cardiologists (ICC 0.74, 0.66) matches theirs with each other (0.67). Because each conclusion traces to a cited guideline and is validated against expert cardiologists, it yields decisions a clinician can audit rather than act on blindly -- a step toward use in continuous monitoring.