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Cited by 15ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Artificial intelligence-enabled electrocardiography from scientific research to clinical application

Open access
Lin, Chin-Sheng, Liu, Wei-Ting, Chen, Yuan-Hao, Lin, Shih-Hua, Lin, Chin·2025·EMBO Molecular Medicine
DOI 10.1038/s44321-025-00351-yPMC PMC12808761
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Abstract

Recent advancements in artificial intelligence (AI) have revolutionized the application of electrocardiography (ECG) in cardiovascular diagnostics. This review highlights the transformative impact of AI on traditional ECG analysis, detailing how deep learning algorithms are overcoming the limitations of human interpretation and conventional diagnostic criteria. Historically, ECG interpretation has relied on well-established, physiologically-based criteria. The advancement of AI-ECG is marked by its capacity to process complex high-dimensional data directly from raw signals, revealing patterns often missed by conventional methods. Notably, AI models have identified signs of asymptomatic low ejection fraction and paroxysmal atrial fibrillation during normal sinus rhythm, enabling earlier clinical intervention. In addition to improved diagnostic utility, AI-ECG offers promising applications in risk stratification and community screening. Several randomized controlled trials (RCTs) have shown that integrating AI into clinical workflows not only reduces critical intervention times but also identifies patients at elevated risk of adverse outcomes. Future directions involve integrating additional clinical data sources, improving model interpretability through explainable AI, and developing unified platforms to manage outputs from multiple models.

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  • Gatekeeper use requires more than discrimination: reliable calibration, very high sensitivity and negative predictive value in the intended population, prospective pathway-level validation, decision-curve evidence at the selected operating threshold, and explicit mechanisms for clinician override and follow-up.

    “Gatekeeper use requires more than discrimination: reliable calibration, very high sensitivity and negative predictive value in the intended population, prospective pathway-level validation, decision-curve evidence at the selected operating threshold, and explicit mechanisms for clinician override and follow-up.36,39,40”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [36]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • What remains unproven is whether a negative or low-risk AI-ECG output can safely support echocardiography deferral.

    “What remains unproven is not detection capability, but whether a negative or low-risk AI-ECG output can safely support echocardiography deferral.3,36,39,40”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [36]

    The Frameworks for AI-ECG deployment section says confirmatory-testing strategies need prospective validation, supporting the general point that clinical pathways remain unproven. It does not specifically address whether a negative or low-risk AI-ECG result can safely justify deferring echocardiography. Page numbers are unavailable in our copy. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Interpretability tools may make model behavior more inspectable, but they do not by themselves resolve questions of accountability, acceptable error, or downstream clinical action in opportunistic screening.

    “Interpretability tools may make model behavior more inspectable, but they do not by themselves resolve questions of accountability, acceptable error, or downstream clinical action in opportunistic screening.”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [36]

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Cited by

Sorted by citations

Authors
Lin, C.-S., Liu, W.-T., Chen, Y.-H., Lin, S.-H., & Lin, C.
Affiliations
Tri-Service General Hospital (TW)
Corresponding author
Lin, Chin
ORCID
Venue
EMBO Molecular Medicine
Publisher
Springer Science and Business Media LLC
Published
2025-12-01
Volume
18
Issue
1
Pages
22-40
ISSN
  • 1757-4676 (ISSN-L)
  • 1757-4684
DOI
10.1038/s44321-025-00351-y
PMC
PMC12808761
OpenAlex
W4416852690
Cited by
15
References
13
Citations by year
2026: 15
FWCI
2.72
Type
journal article
Language
en
Open access
Licence
Locations
  • EMBO Molecular Medicine (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Science and Technology Council (NSTC 114-2321-B-016-005)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence, electrocardiogram, deep learning, ECG interpretation, reduced ejection fraction, paroxysmal atrial fibrillation, risk stratification, community-based screening, explainable AI, clinical workflow integration, cardiovascular diagnostics
MeSH terms
Deep Learning, Artificial Intelligence, Atrial Fibrillation, Cardiovascular Diseases, Electrocardiography, Humans

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