Phenotypic Selectivity of Artificial Intelligence–Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction
Despite being developed to detect specific cardiovascular conditions, AI-ECG models detect the presence and predict the future development of a broad range of cardiovascular diseases with similar propensity. This challenges their role as binary diagnostic tools and instead supports their use as broader cardiovascular biomarkers.
Authors
- Philip M. Croon
- Rohan Khera (ORCID: https://orcid.org/0000-0001-9467-6199)
- Lovedeep Singh Dhingra (ORCID: https://orcid.org/0000-0002-5664-4126)
- Dhruva Biswas (ORCID: https://orcid.org/0000-0001-9141-5188)
- Evangelos K. Oikonomou (ORCID: https://orcid.org/0000-0003-4362-0720)
Institutions
- Yale New Haven Hospital (US)
- Yale University (US)
- Yale New Haven Health System (US)
- Norwegian Womens Public Health Association (NO)
- Amsterdam University Medical Centers (NL)
- Data Management (Italy) (IT)
- Amsterdam University of Applied Sciences (NL)
- University of Amsterdam (NL)
Publication Details
- Journal
- Circulation
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1161/circulationaha.125.076279
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.58