ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents

2026-06-23

AI Summary

This work proposes a multi-agent framework for ECG report generation that emulates clinicians’ iterative diagnostic reasoning. Unlike existing end-to-end approaches—where errors propagate irreversibly—or agent-based systems lacking revision capabilities, our method explicitly links each diagnostic statement to its supporting evidence, enables dynamic incorporation of new contextual information, and allows clinicians to validate and edit individual findings mid-process. The system introduces, for the first time, a traceable, editable, and bidirectionally iterative reporting mechanism that enhances transparency and aligns with real-world clinical workflows. Built upon a deployed ECG analysis model and a cloud-native architecture, it facilitates efficient human–AI collaboration and demonstrates immediate clinical deployability, as validated through four representative interactive case studies.

Abstract

Existing ECG report generation is tightly coupled -- interpretation and reporting fused end-to-end, so errors propagate without stage-level recourse -- while agent-based systems decouple tasks but remain single-pass, never revisiting earlier outputs. Clinical ECG reporting instead unfolds iteratively, requiring progressive context integration and bidirectional editing. We present \textsc{ATRIA}, a multi-agent ECG reporting system that mirrors the clinician's iterative workflow: it binds every report claim to its supporting evidence, flags statements unsupported by that evidence, incorporates additional context mid-session, and lets clinicians verify and revise individual findings rather than accept one opaque output. Because its agents use ECG analysis models already in clinical use, the underlying findings are clinically trustworthy; and as a cloud-based web service, \textsc{ATRIA} is ready for immediate deployment. We demonstrate \textsc{ATRIA} through four interaction cases, with a live demo and video available.

Problem

• ECG report generation
• error propagation
• iterative workflow
• clinical reporting
• multi-agent system

Innovation

• iterative multi-agent system
• traceable ECG reporting
• evidence-grounded claims
• interactive clinical AI
• adaptive report generation