Currently submitted to: Journal of Medical Internet Research
Date Submitted: Mar 11, 2026
Open Peer Review Period: Mar 12, 2026 - May 7, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial intelligence in cardiovascular diseases: applications in diagnosis, prevention, and clinical management
ABSTRACT
Background:
Cardiovascular diseases continue to be one of the leading causes of morbidity and mortality worldwide, posing significant challenges for early diagnosis, risk stratification, and clinical follow-up. In this context, the expansion of digital health ecosystems has favored the incorporation of artificial intelligence-based tools capable of analyzing large volumes of clinical and physiological data. These technologies have the potential to support clinical decision-making, optimize preventive strategies, and personalize therapeutic management.
Objective:
To explore and map the available evidence on the use of artificial intelligence in the management of cardiovascular diseases, including its applications in prevention, diagnosis, and clinical management.
Methods:
A scoping review was conducted following the methodological framework of Arksey and O'Malley and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline. A systematic search was conducted in PubMed, Scopus, and BIREME for studies published between 2018 and 2025 in English and Spanish. The search strategy combined controlled vocabulary and free terms using Boolean operators. Studies evaluating the use of artificial intelligence in the prevention, diagnosis, or management of cardiovascular diseases were included. The results were summarized descriptively according to the main areas of application.
Results:
A total of 2,007 records were identified, of which 35 studies met the inclusion criteria after the selection process. The available evidence shows that artificial intelligence applications in cardiology are mainly focused on cardiovascular risk stratification, algorithm-assisted diagnosis, especially through electrocardiography and cardiovascular imaging, and clinical decision support. Likewise, some research explores its use in therapeutic personalization and remote monitoring using digital devices. However, most studies are retrospective designs or methodological evaluations, with limited evidence on their impact on hard clinical outcomes.
Conclusions:
The evidence synthesised in this scoping review indicates that artificial intelligence is emerging as a relevant tool in cardiovascular care, with applications in risk stratification, early diagnosis, therapeutic support, and patient monitoring. Although many studies report improvements in intermediate outcomes such as diagnostic accuracy and risk prediction, evidence demonstrating consistent benefits in major clinical outcomes remains limited. Future research should prioritise prospective studies and real-world evaluations to better define the role of artificial intelligence in cardiovascular practice. Clinical Trial: Not applicable
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