Artificial intelligence in cardiovascular prognosis and diagnosis: a review
Cardiovascular diseases (CVDs) are a leading cause of mortality globally, necessitating innovative approaches for improved diagnosis, prognosis, and treatment. Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized cardiovascular medicine by leveraging vast mul...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Open Exploration Publishing Inc.
2025-07-01
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| Series: | Exploration of Medicine |
| Subjects: | |
| Online Access: | https://www.explorationpub.com/uploads/Article/A1001347/1001347.pdf |
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| Summary: | Cardiovascular diseases (CVDs) are a leading cause of mortality globally, necessitating innovative approaches for improved diagnosis, prognosis, and treatment. Recent advances in artificial intelligence (AI) and machine learning (ML) have revolutionized cardiovascular medicine by leveraging vast multi-modal datasets—including genetic markers, imaging, and electronic health records (EHRs)—to provide patient-specific insights. This review highlights the transformative potential of AI applications, such as AI-enabled electrocardiograms (ECGs) and deep learning (DL)-based analysis, in enhancing diagnostic and prognostic accuracy and personalizing patient care. Notable progress includes predictive models for a variety of CVDs, including ischemic heart disease, atrial fibrillation, and heart failure, with performance metrics significantly surpassing traditional methods. Emerging technologies, such as explainable AI, large language models, and digital-twin technologies, further expand the horizons of precision cardiology. This paper also discusses challenges facing the AI and ML applications in CVDs and promising future directions. |
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| ISSN: | 2692-3106 |