ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents

Donggyun Hong; Junmyung Kwon; Kyuhwan Lee; Yong-yeon Jo

arxiv: 2606.24392 · v2 · pith:2CTXVGEEnew · submitted 2026-06-23 · 💻 cs.AI

ATRIA: Adaptive Traceable ECG Reporting with Iterative Agents

Donggyun Hong , Kyuhwan Lee , Junmyung Kwon , Yong-Yeon Jo This is my paper

Pith reviewed 2026-07-01 06:53 UTC · model grok-4.3

classification 💻 cs.AI

keywords ECG report generationmulti-agent systemsiterative workflowtraceabilityevidence bindingclinical decision supportreport revision

0 comments

The pith

ATRIA is a multi-agent system that generates ECG reports by binding each claim to evidence and supporting iterative clinician revisions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing ECG report generation fuses interpretation and reporting end-to-end, allowing errors to propagate without stage-level fixes, while agent-based alternatives stay single-pass and never revisit earlier outputs. ATRIA creates a multi-agent setup that follows the iterative clinical workflow by attaching evidence to every claim, flagging unsupported statements, adding context mid-session, and permitting verification or revision of individual findings rather than one opaque result. This setup uses ECG analysis models already in clinical use. The system runs as a cloud-based web service ready for deployment. Four interaction cases demonstrate how the process unfolds in practice.

Core claim

We present 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, ATRIA is ready for immediate deployment.

What carries the argument

The multi-agent architecture that decouples tasks while enabling evidence binding, unsupported-statement flagging, mid-session context integration, and selective revision of findings.

If this is right

Every report claim becomes traceable to specific evidence from the ECG analysis models.
Unsupported statements are automatically identified and surfaced for review.
New clinical context can be added during an ongoing session without restarting the entire process.
Clinicians can verify and revise individual findings rather than the full report output.
The system is available immediately as a cloud-based web service.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same evidence-binding and iterative-revision structure could be tested on other diagnostic report types such as radiology or pathology notes.
The flagging mechanism might create audit trails useful for training or liability review in AI-assisted diagnostics.
Deployment data could show how frequently clinicians actually supply mid-session context changes in real workflows.

Load-bearing premise

The assumption that ECG analysis models already in clinical use supply reliable base findings that the agents can reliably build upon.

What would settle it

A controlled test case in which the system produces a report containing a claim that has no matching evidence in the input ECG data yet fails to flag that claim as unsupported.

Figures

Figures reproduced from arXiv: 2606.24392 by Donggyun Hong, Junmyung Kwon, Kyuhwan Lee, Yong-yeon Jo.

**Figure 2.** Figure 2: Overview of ATRIA. Five agents coordinate over a shared artifact store that every agent reads from and writes to, preserving intermediate outputs across the staged workflow. retrieved evidence. (4) For a follow-up request—clarification, context attachment, comparison, evidence, or revision—the orchestrator dispatches only the agents whose artifacts are affected, without re-executing the full pipeline unl… view at source ↗

**Figure 1.** Figure 1: Screenshot of ATRIA chat interface after an ECG upload: the Orchestrator Agent dispatches the staged workflow, with each agent step streaming in order as artifacts are produced. run. Three requirements distinguish it from single-pass pipelines— stage-level traceability, progressive context integration, and bidirectional iterative use—and motivate two architectural decisions: stagelevel handoffs in which… view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ATRIA sketches an iterative multi-agent ECG reporting system with evidence binding and flagging, but supplies only qualitative demos and no measurements of whether the new layers improve or preserve output quality.

read the letter

The core idea is a multi-agent workflow that breaks ECG reporting into steps, ties each claim to source evidence, flags unsupported parts, and allows mid-process context or edits. This directly targets the gap between end-to-end models that hide mistakes and single-pass agents that never revisit earlier outputs.

The architecture description matches real clinical practice reasonably well on paper. Binding claims to evidence and permitting targeted revisions are practical features that existing systems often lack.

The soft spots are straightforward. There are no metrics, no baseline comparisons, no ablation on the orchestration or binding steps, and no error analysis. The four interaction cases are demonstrations only. The claim that clinical trustworthiness follows automatically from reusing existing ECG models is asserted without testing whether the new agent layer introduces, masks, or propagates errors. The stress-test note on this point holds up.

Because the full manuscript was not accessible, implementation details and any unreported experiments remain unknown. The work is still at the system-description stage.

This is the sort of paper that might interest groups already building medical agent tools, but it does not yet contain results that would change practice or justify citation. It does not deserve peer review in its current form; the authors would need to add quantitative validation and controlled comparisons first.

Referee Report

2 major / 0 minor

Summary. The paper introduces ATRIA, a multi-agent ECG reporting system designed to emulate clinicians' iterative workflow. It decouples interpretation and reporting to enable binding each claim to supporting evidence, flagging unsupported statements, incorporating additional context mid-session, and allowing verification/revision of individual findings. The system is claimed to be clinically trustworthy because its agents invoke existing ECG analysis models, and it is presented as ready for immediate cloud-based deployment, with demonstration via four qualitative interaction cases and a live demo.

Significance. If the iterative binding, flagging, and revision mechanisms can be shown to preserve or improve upon the reliability of the underlying ECG models without introducing new error modes, ATRIA could address a practical limitation in current AI-assisted reporting by providing stage-level recourse and traceability. The absence of any quantitative evaluation, however, prevents assessment of whether these features deliver measurable clinical benefit.

major comments (2)

[Abstract] Abstract: The assertion that 'the underlying findings are clinically trustworthy' because agents use ECG analysis models already in clinical use is presented without any error analysis, ablation study, or comparison showing that the multi-agent orchestration, evidence binding, and flagging steps preserve base-model reliability or reduce propagated errors.
[Abstract] Abstract (final sentence): The claim of readiness for immediate deployment rests on the four interaction cases, which are described only qualitatively; no metrics, user studies, or validation experiments are supplied to demonstrate that the iterative features improve report quality, reduce errors, or outperform single-pass or end-to-end baselines.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract claims. We agree that the current wording makes assertions that exceed the quantitative evidence supplied in the manuscript and will revise the abstract to reflect the work's scope as a system demonstration.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that 'the underlying findings are clinically trustworthy' because agents use ECG analysis models already in clinical use is presented without any error analysis, ablation study, or comparison showing that the multi-agent orchestration, evidence binding, and flagging steps preserve base-model reliability or reduce propagated errors.

Authors: The manuscript provides no error analysis, ablation studies, or comparisons demonstrating that the multi-agent orchestration, evidence binding, or flagging preserve base-model reliability or reduce new error modes. The original phrasing relied on the use of established clinical models for interpretation but does not address orchestration effects. We will revise the abstract to remove or qualify this assertion. revision: yes
Referee: [Abstract] Abstract (final sentence): The claim of readiness for immediate deployment rests on the four interaction cases, which are described only qualitatively; no metrics, user studies, or validation experiments are supplied to demonstrate that the iterative features improve report quality, reduce errors, or outperform single-pass or end-to-end baselines.

Authors: The manuscript demonstrates ATRIA via four qualitative interaction cases, a live demo, and video, without metrics, user studies, or baseline comparisons. We agree this does not support claims of immediate deployment readiness or measurable improvements from the iterative features. We will revise the final sentence to describe the demonstration without asserting deployment readiness. revision: yes

Circularity Check

0 steps flagged

No circularity: system architecture with no derivations or fitted quantities

full rationale

The paper presents an architectural description of a multi-agent ECG reporting system. No equations, parameters, or derivations appear in the provided text. The central claim that outputs are clinically trustworthy rests on the assumption that base ECG models remain reliable under orchestration, but this is not a self-referential reduction or fitted prediction; it is an external premise. No steps match any enumerated circularity pattern.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review contains no mathematical content, fitted parameters, or new postulated entities; the central claims rest on the unverified assertion that existing clinical ECG models confer trustworthiness.

pith-pipeline@v0.9.1-grok · 5686 in / 1095 out tokens · 33739 ms · 2026-07-01T06:53:02.376140+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Reference graph

Works this paper leans on

21 extracted references · 13 canonical work pages · 1 internal anchor

[1]

Hyunseung Chung, Jungwoo Oh, Daeun Kyung, Jiho Kim, Yeonsu Kwon, Min- Gyu Kim, and Edward Choi. 2026. ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue.IEEE Xplore(2026). https://ieeexplore.ieee.org/ document/11464123 IEEE Xplore document 11464123; corresponding arXiv preprint: arXiv:2601.20323

work page arXiv 2026
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2026
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Awni Y Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H Tison, Colin Bourn, Mintu P Turakhia, and Andrew Y Ng. 2019. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.Nature Medicine25, 1 (2019), 65–69

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Joo Hee Jeong, Sora Kang, Hak Seung Lee, Min Sung Lee, Jeong Min Son, Joon- Myung Kwon, Hyoung Seok Lee, Yun Young Choi, So Ree Kim, Dong-Hyuk Cho, Yun Gi Kim, Mi-Na Kim, Jaemin Shim, Seong-Mi Park, Young-Hoon Kim, and Jong-Il Choi. 2024. Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventric...

work page doi:10.1093/ehjdh/ztae062 2024
[5]

Joon-Myoung Kwon, Soo Youn Lee, Ki-Hyun Jeon, Yeha Lee, Kyung-Hee Kim, Jinsik Park, Byung-Hee Oh, and Myong-Mook Lee. 2020. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.Journal of the American Heart Association9, 7 (2020), e014717. doi:10.1161/JAHA.119.014717

work page doi:10.1161/jaha.119.014717 2020
[6]

Xiang Lan, Feng Wu, Kai He, Qinghao Zhao, Shenda Hong, and Mengling Feng
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Gem: Empowering mllm for grounded ecg understanding with time series and images.arXiv preprint arXiv:2503.06073(2025)

work page arXiv 2025
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Min Sung Lee, Tae Gun Shin, Youngjoo Lee, Dong Hoon Kim, Sung Hyuk Choi, Hanjin Cho, Mi Jin Lee, Ki Young Jeong, Won Young Kim, Young Gi Min, Chul Han, Jae Chol Yoon, Eujene Jung, Woo Jeong Kim, Chiwon Ahn, Jeong Yeol Seo, Tae Ho Lim, Jae Seong Kim, Jeff Choi, Joon-Myoung Kwon, and Kyuseok Kim. 2025. Artificial intelligence applied to electrocardiogram to...

work page doi:10.1093/eurheartj/ehaf004 2025
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Life in the Fast Lane. [n. d.]. ECG Library. https://litfl.com/ecg-library/. Accessed: 2026-06-02

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Jaehyun Lim, Min Sung Lee, Jung Ho Suh, Sora Kang, Hak Seung Lee, Jong-Hwan Jang, Jeong Min Son, Joon-Myoung Kwon, Yong-Jin Kim, Kyung-Hee Kim, and Seung-Pyo Lee. 2026. Artificial Intelligence-Enabled ECG for Elevated E/e’ on Echocardiography: Hemodynamic Relevance and Prognostic Value.Journal of the American Heart Association15, 9 (2026), e046989. doi:10...

work page doi:10.1161/jaha.125.046989 2026
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J. Warren Mason, E. William Hancock, Leonard S. Gettes, James J. Bailey, Rory Childers, Barbara J. Deal, Mark Josephson, Paul Kligfield, Jan A. Kors, Peter Macfarlane, Olle Pahlm, David M. Mirvis, Peter Okin, Pentti Rautaharju, Gerard van Herpen, Galen S. Wagner, and Hein Wellens. 2007. Recommendations for the Standardization and Interpretation of the Ele...

work page doi:10.1161/circulationaha.106.180201 2007
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Jaeho Park, TaeJun Park, Joon-myoung Kwon, and Yong-Yeon Jo. 2025. Bench- marking ECG Delineation using Deep Neural Network-based Semantic Seg- mentation Models. InProceedings of the sixth Conference on Health, Inference, and Learning (Proceedings of Machine Learning Research, Vol. 287), Xuhai Or- son Xu, Edward Choi, Pankhuri Singhal, Walter Gerych, Shen...

2025
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Tae-Min Rhee, Sora Kang, Min Sung Lee, Ga In Han, Ah-Hyun Yoo, Jong-Hwan Jang, Yong-Yeon Jo, Jeong Min Son, Joon-Myoung Kwon, Su-Yeon Choi, Hak Se- ung Lee, and Heesun Lee. 2026. Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.JACC: Advances5, 4 (2026), 102660. doi...

work page doi:10.1016/j.jacadv.2026.102660 2026
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António H Ribeiro, Manoel H Ribeiro, Gabriele M M Paixão, Derick M Oliveira, Paulo R Gomes, José A Canazart, M Ferreira, Carl R Andersson, Peter W Macfar- lane, Patrick Wagner, et al. 2020. Automatic diagnosis of the 12-lead ECG using a deep neural network.Nature Communications11, 1 (2020), 1760

2020
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Jürg Schläpfer and Hein J. Wellens. 2017. Computer-Interpreted Electrocardio- grams: Benefits and Limitations.Journal of the American College of Cardiology 70, 9 (2017), 1183–1192. doi:10.1016/j.jacc.2017.07.723

work page doi:10.1016/j.jacc.2017.07.723 2017
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Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, and Aaqib Saeed. 2024. Electrocar- diogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling. arXiv:2409.08788 [cs.LG] https://arxiv.org/abs/2409. 08788

work page arXiv 2024
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UC Irvine and ASU and CSU authors. 2026. CARE-ECG: Causal Agent- based Reasoning for Explainable and Counterfactual ECG Interpretation. arXiv:2604.10420 [cs.LG] https://arxiv.org/abs/2604.10420

work page internal anchor Pith review Pith/arXiv arXiv 2026
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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Zhenwu Peng, Jie Fu, Rossella Arcucci, Huaxiu Yao, and Mi Zhang. 2024. MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation.arXiv preprint arXiv:2403.04945(2024). https://arxiv.org/abs/2403. 04945

work page arXiv 2024
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WikiDoc. [n. d.]. ECG Criteria. https://www.wikidoc.org/index.php/ECG_ Criteria. Accessed: 2026-06-02

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Han Yu, Peikun Guo, and Akane Sano. 2023. Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation. InProceedings of the 3rd Machine Learning for Health Symposium (Proceedings of Machine Learning Research, Vol. 225). PMLR, 650–663. https://proceedings.mlr.press/v225/yu23b. html

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Jin Yu, JaeHo Park, TaeJun Park, Gyurin Kim, JiHyun Lee, Min Sung Lee, Joon- myoung Kwon, Jeong Min Son, and Yong-Yeon Jo. 2025. ALFRED: Ask a Large- language model For Reliable ECG Diagnosis.arXiv preprint arXiv:2505.03781 (2025)

work page arXiv 2025

[1] [1]

Hyunseung Chung, Jungwoo Oh, Daeun Kyung, Jiho Kim, Yeonsu Kwon, Min- Gyu Kim, and Edward Choi. 2026. ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue.IEEE Xplore(2026). https://ieeexplore.ieee.org/ document/11464123 IEEE Xplore document 11464123; corresponding arXiv preprint: arXiv:2601.20323

work page arXiv 2026

[2] [2]

ECGwaves. [n. d.]. The ECG Book. https://ecgwaves.com/course/the-ecg-book/. Accessed: 2026-06-02

2026

[3] [3]

Awni Y Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H Tison, Colin Bourn, Mintu P Turakhia, and Andrew Y Ng. 2019. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.Nature Medicine25, 1 (2019), 65–69

2019

[4] [4]

Joo Hee Jeong, Sora Kang, Hak Seung Lee, Min Sung Lee, Jeong Min Son, Joon- Myung Kwon, Hyoung Seok Lee, Yun Young Choi, So Ree Kim, Dong-Hyuk Cho, Yun Gi Kim, Mi-Na Kim, Jaemin Shim, Seong-Mi Park, Young-Hoon Kim, and Jong-Il Choi. 2024. Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventric...

work page doi:10.1093/ehjdh/ztae062 2024

[5] [5]

Joon-Myoung Kwon, Soo Youn Lee, Ki-Hyun Jeon, Yeha Lee, Kyung-Hee Kim, Jinsik Park, Byung-Hee Oh, and Myong-Mook Lee. 2020. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.Journal of the American Heart Association9, 7 (2020), e014717. doi:10.1161/JAHA.119.014717

work page doi:10.1161/jaha.119.014717 2020

[6] [6]

Xiang Lan, Feng Wu, Kai He, Qinghao Zhao, Shenda Hong, and Mengling Feng

[7] [7]

Gem: Empowering mllm for grounded ecg understanding with time series and images.arXiv preprint arXiv:2503.06073(2025)

work page arXiv 2025

[8] [8]

Min Sung Lee, Tae Gun Shin, Youngjoo Lee, Dong Hoon Kim, Sung Hyuk Choi, Hanjin Cho, Mi Jin Lee, Ki Young Jeong, Won Young Kim, Young Gi Min, Chul Han, Jae Chol Yoon, Eujene Jung, Woo Jeong Kim, Chiwon Ahn, Jeong Yeol Seo, Tae Ho Lim, Jae Seong Kim, Jeff Choi, Joon-Myoung Kwon, and Kyuseok Kim. 2025. Artificial intelligence applied to electrocardiogram to...

work page doi:10.1093/eurheartj/ehaf004 2025

[9] [9]

Life in the Fast Lane. [n. d.]. ECG Library. https://litfl.com/ecg-library/. Accessed: 2026-06-02

2026

[10] [10]

Jaehyun Lim, Min Sung Lee, Jung Ho Suh, Sora Kang, Hak Seung Lee, Jong-Hwan Jang, Jeong Min Son, Joon-Myoung Kwon, Yong-Jin Kim, Kyung-Hee Kim, and Seung-Pyo Lee. 2026. Artificial Intelligence-Enabled ECG for Elevated E/e’ on Echocardiography: Hemodynamic Relevance and Prognostic Value.Journal of the American Heart Association15, 9 (2026), e046989. doi:10...

work page doi:10.1161/jaha.125.046989 2026

[11] [11]

Warren Mason, E

J. Warren Mason, E. William Hancock, Leonard S. Gettes, James J. Bailey, Rory Childers, Barbara J. Deal, Mark Josephson, Paul Kligfield, Jan A. Kors, Peter Macfarlane, Olle Pahlm, David M. Mirvis, Peter Okin, Pentti Rautaharju, Gerard van Herpen, Galen S. Wagner, and Hein Wellens. 2007. Recommendations for the Standardization and Interpretation of the Ele...

work page doi:10.1161/circulationaha.106.180201 2007

[12] [12]

Jaeho Park, TaeJun Park, Joon-myoung Kwon, and Yong-Yeon Jo. 2025. Bench- marking ECG Delineation using Deep Neural Network-based Semantic Seg- mentation Models. InProceedings of the sixth Conference on Health, Inference, and Learning (Proceedings of Machine Learning Research, Vol. 287), Xuhai Or- son Xu, Edward Choi, Pankhuri Singhal, Walter Gerych, Shen...

2025

[13] [13]

Tae-Min Rhee, Sora Kang, Min Sung Lee, Ga In Han, Ah-Hyun Yoo, Jong-Hwan Jang, Yong-Yeon Jo, Jeong Min Son, Joon-Myoung Kwon, Su-Yeon Choi, Hak Se- ung Lee, and Heesun Lee. 2026. Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.JACC: Advances5, 4 (2026), 102660. doi...

work page doi:10.1016/j.jacadv.2026.102660 2026

[14] [14]

António H Ribeiro, Manoel H Ribeiro, Gabriele M M Paixão, Derick M Oliveira, Paulo R Gomes, José A Canazart, M Ferreira, Carl R Andersson, Peter W Macfar- lane, Patrick Wagner, et al. 2020. Automatic diagnosis of the 12-lead ECG using a deep neural network.Nature Communications11, 1 (2020), 1760

2020

[15] [15]

Jürg Schläpfer and Hein J. Wellens. 2017. Computer-Interpreted Electrocardio- grams: Benefits and Limitations.Journal of the American College of Cardiology 70, 9 (2017), 1183–1192. doi:10.1016/j.jacc.2017.07.723

work page doi:10.1016/j.jacc.2017.07.723 2017

[16] [16]

Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, and Aaqib Saeed. 2024. Electrocar- diogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling. arXiv:2409.08788 [cs.LG] https://arxiv.org/abs/2409. 08788

work page arXiv 2024

[17] [17]

UC Irvine and ASU and CSU authors. 2026. CARE-ECG: Causal Agent- based Reasoning for Explainable and Counterfactual ECG Interpretation. arXiv:2604.10420 [cs.LG] https://arxiv.org/abs/2604.10420

work page internal anchor Pith review Pith/arXiv arXiv 2026

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Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao, Hui Shen, Zhenwu Peng, Jie Fu, Rossella Arcucci, Huaxiu Yao, and Mi Zhang. 2024. MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation.arXiv preprint arXiv:2403.04945(2024). https://arxiv.org/abs/2403. 04945

work page arXiv 2024

[19] [19]

WikiDoc. [n. d.]. ECG Criteria. https://www.wikidoc.org/index.php/ECG_ Criteria. Accessed: 2026-06-02

2026

[20] [20]

Han Yu, Peikun Guo, and Akane Sano. 2023. Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation. InProceedings of the 3rd Machine Learning for Health Symposium (Proceedings of Machine Learning Research, Vol. 225). PMLR, 650–663. https://proceedings.mlr.press/v225/yu23b. html

2023

[21] [21]

Jin Yu, JaeHo Park, TaeJun Park, Gyurin Kim, JiHyun Lee, Min Sung Lee, Joon- myoung Kwon, Jeong Min Son, and Yong-Yeon Jo. 2025. ALFRED: Ask a Large- language model For Reliable ECG Diagnosis.arXiv preprint arXiv:2505.03781 (2025)

work page arXiv 2025