ZBeats Inc / New York University / Stony Brook Medicine / University of Pennsylvania / Binghamton University (Zhou, Zhang, Xi et al.)
Training code and model weights not public. Contact creators for more information.
Multi-agent LLM framework, deployed as a Software-as-a-Medical-Device on AWS, that assists cardiologists reading 24-hour Holter/patch ECG monitoring studies. Three fine-tuned LLM agents divide the diagnostic workflow the way a cardiologist would: a table-to-text agent (Llama-3.1-8B) extracts findings from tabular arrhythmia metrics, an image-to-text agent (LLaVA-v1.5-13B) extracts findings from ECG tracing images, and a findings-to-interpretation agent (Llama-3.1-8B) synthesizes both against clinical guidelines with a fact-checking step. Each agent is instruction-tuned on cardiologist-adjudicated reports from 2,000+ real-world patients and further steered at inference with in-context demonstrations matched to the patient's age, sex and arrhythmia class. In blinded cardiologist ratings across eight clinical/security metrics (1-5 scale), ZODIAC outperformed GPT-4o, Gemini-Pro, Llama-3.1-405B, Mixtral-8x22B, and medical-specialist LLMs (BioGPT, Meditron, Med42) on every metric while using under 30B total parameters, and has been integrated into commercial ECG monitoring devices. This is a proprietary product; no public code or model weights have been released.
Architecture
Hybrid
Three task-specialized, individually instruction-fine-tuned LLM agents: a table-to-findings agent (Llama-3.1-8B), an ECG-image-to-findings agent (LLaVA-v1.5-13B), and a findings-to-interpretation agent (Llama-3.1-8B) with guideline fact-checking
Framework
PyTorch
Added to catalog
2026-08-10
ZODIAC Clinical Validation Cohort (undisclosed institutions)
2,000+ real-world Holter/patch-monitoring patients (24-hour ECG tracings plus tabular arrhythmia metrics) collected under IRB approval from unnamed collaborating institutions; 47.1% female / 52.9% male, spanning adult (30.3%), middle-aged (27.1%) and geriatric (42.6%) age groups and normal (25.5%), clinically-significant (40.8%) and life-threatening (33.7%) arrhythmia classes.
Generates itemized clinical findings and diagnostic interpretation text from 24-hour ECG Holter/patch monitoring metrics and tracing images, spanning arrhythmia types (AF/AFL, PVC, SVT, VT, AV block, pause)