ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents

ATRIA, a multi-agent ECG reporting system by Donggyun Hong, Kyuhwan Lee, Junmyung Kwon and Yong-Yeon Jo, was submitted to arXiv on 23 Jun 2026 as arXiv:2606.24392. The system mirrors a clinician's iterative ECG workflow, binds every report claim to its supporting evidence, flags unsupported statements, and enables mid-session context updates and targeted edits.

What is ATRIA and who built it?

ATRIA is a cloud-based web service that implements a multi-agent ECG reporting workflow, authored by Donggyun Hong, Kyuhwan Lee, Junmyung Kwon and Yong-Yeon Jo and submitted to arXiv on 23 Jun 2026 (arXiv:2606.24392). The submission file on arXiv shows the first version was posted on Tue, 23 Jun 2026 (573 KB). The paper presents ATRIA as ready for immediate deployment and demonstrates it in four interaction cases with a live demo and video available.

ATRIA's design purpose is explicit: to decouple ECG interpretation and reporting into agents that can iterate, revisit earlier outputs, and expose the evidence behind each claim. The authors contrast this with tightly coupled end-to-end report generation, which they argue allows errors to propagate without stage-level recourse, and with previous agent-based systems that remain single-pass.

How does ATRIA work?

ATRIA arranges iterative agents to mirror clinical reporting: agents produce findings using ECG analysis models already in clinical use, bind each report claim to supporting evidence, flag statements unsupported by that evidence, accept additional context mid-session, and let clinicians verify and revise individual findings rather than accept one opaque output. The system is presented as a cloud-based web service, implying a client-server interaction where clinicians interact with the orchestrated agents.

The paper emphasizes four capabilities. First, agents use ECG analysis models already deployed in clinical settings, so the underlying findings are described as clinically trustworthy. Second, every report claim is bound to its evidence and any unsupported statements are flagged. Third, ATRIA supports incorporation of additional contextual information during an active session. Fourth, clinicians can verify and revise individual findings instead of receiving a single monolithic report.

Why it matters

ATRIA addresses two persistent problems in automated ECG reporting: opaque end-to-end outputs and single-pass agent systems. By binding claims to evidence and allowing mid-session context and edits, ATRIA gives clinicians granular control over each finding and an auditable trail for decisions. That combination could reduce downstream error propagation and make automated reports more compatible with clinical workflows that expect iterative review and correction.

Using ECG analysis models already in clinical use matters because it focuses the contribution on workflow and traceability rather than novel diagnostic models; ATRIA layers iterative, traceable reporting around existing analyzers. The authors also supply concrete demonstrations: the paper includes four interaction cases plus a live demo and video to show the interaction patterns they describe.

What to watch

Look for the public demo and video the authors provide to assess how ATRIA presents evidence bindings and revision UI in practice, and whether the system integrates with widely used ECG analysis models as claimed. Adoption signals will include pilot deployments of the cloud-based web service and follow-on papers or code releases tied to the arXiv entry (arXiv:2606.24392).

References and factual anchors from the submission: the arXiv entry lists the four authors, the submission date (23 Jun 2026), the identifier (arXiv:2606.24392), the [v1] upload timestamp and file size (573 KB), and the abstract summary that ATRIA is a multi-agent, traceable, cloud-based ECG reporting system demonstrated via four interaction cases with a live demo and video available.