Artificial intelligence and the electrocardiogram: A modern renaissance
Abstract
Integrating Artificial Intelligence (AI) with electrocardiograms (ECG) represents a transformative shift in cardiovascular medicine, marking a modern renaissance of this traditional diagnostic technique. This article explores recent advancements in AI-enhanced ECG technologies, highlighting their potential to improve diagnostic accuracy, predict cardiovascular events, and enable personalized patient care. AI-driven ECG interpretation has demonstrated groundbreaking capabilities in the field of both structural and electrical heart diseases. Furthermore, deep learning techniques have expanded diagnostic capabilities by identifying subtle ECG patterns invisible to the human eye, improving the detection of several cardiac disorders. The increasing integration of AI-ECG into wearable technologies extends cardiac monitoring beyond conventional clinical settings, providing continuous, real-time health assessment. Despite these advancements, the widespread adoption of AI-ECG faces several challenges, such as the need for high-quality training data, ensuring algorithm generalizability across diverse populations, addressing bias in model training, and meeting critical regulatory and ethical standards. Moreover, concerns regarding explainability, physician deskilling, legal accountability, and the lack of high-quality studies proving improved patient outcomes remain key obstacles. By enhancing precision in detecting cardiovascular conditions and expanding access to proactive heart healt…
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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 [40]
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 [40]
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Tang, Q., Deng, K., Cui, Y., Liu, H., & Qian, B. (2026) · International Journal of General Medicine · doi:10.2147/ijgm.s610298
“3,36,39,40 Valvular Heart Disease Among non-LVEF applications, valvular heart disease—particularly aortic stenosis (AS)—has emerged as one of the more mature domains for AI-ECG screening.”
- Authors
- Palermi, S., Vecchiato, M., Ng, F. S., Attia, Z., Cho, Y., Anselmino, M., De Ferrari, G. M., Saglietto, A., Sau, A., Chiu, I.-M., Farina, J. M., Arsanjani, R., Zoccai, G. B., Galiuto, L., Nissardi, L., & Dusi, V.
- Affiliations
- Saint Camillus International University of Health and Medical Sciences (IT)
- University of Padua (IT)
- Imperial College Healthcare NHS Trust (GB)
- Mayo Clinic (US)
- Seoul National University (KR)
- Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT)
- National Health Service (GB)
- Cedars-Sinai Medical Center (US)
- Mayo Clinic in Arizona (US)
- Sapienza University of Rome (IT)
- Corresponding author
- Saglietto, Andrea
- ORCID
- Venue
- European Journal of Internal Medicine
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 140
- Pages
- 106329
- ISSN
- 0953-6205 (ISSN-L)
- OpenAlex
- W4410518913
- Cited by
- 42
- References
- 7
- Citations by year
- 2026: 31 · 2025: 11
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Funders
- European Commission
- Kaohsiung Chang Gung Memorial Hospital
- Cedars-Sinai Medical Center
- Sapienza Università di Roma
- Department of Emergency Medicine, Baylor College of Medicine
- Ministero dell’Istruzione, dell’Università e della Ricerca (PNRR - M4C2-I1.3, PE_00000019)
- 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, cardiovascular disease diagnosis, cardiovascular event prediction, wearable cardiac monitoring, algorithm generalizability, algorithmic bias, explainability, training data quality
- MeSH terms
- Wearable Electronic Devices, Deep Learning, Artificial Intelligence, Cardiovascular Diseases, Electrocardiography, Humans
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