Artificial Intelligence in Cardiovascular Diseases: Toward Personalized Diagnosis and Treatment

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Diagnostics in Personalized Medicine".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 1060

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Guest Editor
Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
Interests: general cardiology; interventional cardiology; cardiovascular imaging (CT and MRI); intravascular imaging (IVUS and OCT); artificial intelligence applications in cardiovascular medicine
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Special Issue Information

Dear Colleagues,

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, placing immense pressure on healthcare systems and societies. As diagnostic modalities, therapeutic options, and prevention strategies continue to evolve, the integration of advanced computational methods has emerged as a powerful catalyst for progress. Artificial intelligence (AI), encompassing machine learning, deep learning, and data‐driven modeling, offers unprecedented opportunities to enhance precision medicine, streamline clinical workflows, and translate extensive biological and clinical data into actionable insights.

The application of computational methods to cardiovascular medicine dates back several decades, beginning with early statistical models that supported risk prediction and clinical decision-making. Over time, innovations in imaging, signal processing, and data science paved the way for more sophisticated computer-assisted diagnostic tools. In recent years, the rapid expansion of digital health, electronic medical records, and imaging repositories—combined with breakthroughs in neural networks and cloud computing—has transformed AI from a theoretical possibility to a practical reality. Today, AI enhances image interpretation, improves arrhythmia detection, supports hemodynamic modeling, enables predictive analytics, and guides personalized treatment planning.

This special edition aims to showcase cutting-edge developments, critical perspectives, and transformative clinical applications of artificial intelligence across the cardiovascular continuum. We seek to highlight the breadth of AI’s impact—from foundational algorithmic innovations to their translation into routine practice. This issue provides a forum for clinicians, data scientists, engineers, and translational researchers to collectively explore the current capabilities, challenges, and future directions of AI in cardiovascular care. Topics of interest include technological advancements, clinical utility, ethical considerations, regulatory frameworks, and the societal implications of AI-enabled healthcare.

The contributions in this special edition examine emerging technologies that are redefining cardiovascular diagnosis, prognosis, and therapeutics. These include state-of-the-art imaging analysis, real-time physiological monitoring, multimodal data integration, predictive analytics for disease progression, and generative models for device design and therapeutic planning. Novel frameworks in explainable AI, federated learning, and bias mitigation demonstrate how AI can be responsibly deployed to improve equity and patient outcomes. Together, these works illustrate the transformative potential of AI to deliver more timely, accurate, and personalized cardiovascular care.

We welcome high-quality submissions spanning the spectrum of AI research in cardiovascular medicine, including, but not limited to, the following:

  • Original research articles reporting novel methodologies, clinical applications, or translational findings;
  • Reviews that synthesize current knowledge, emerging trends, and future directions;
  • Research describing innovative algorithms, datasets, or software tools;
  • Studies on AI implementation and evaluation in real-world settings;
  • Research addressing regulatory, ethical, educational, or policy considerations;
  • Interdisciplinary works highlighting collaborations between clinicians, data scientists, and engineers.

Through this special edition, we aim to inspire further innovation and foster dialogue among diverse stakeholders committed to advancing the field. The integration of artificial intelligence into cardiovascular medicine represents not only a scientific frontier but an opportunity to fundamentally improve patient care worldwide. We invite you to contribute to this timely and impactful collection.

Dr. Diaa A. Hakim
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • cardiovascular diseases
  • personalized treatment

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Published Papers (1 paper)

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Research

15 pages, 1450 KB  
Article
Value of Coronary CT Angiography in Ruling Out Coronary Artery Disease in Elderly Patients Candidates to TAVI
by Mattia Alexis Amico, Andrea Taddei, Matteo Casini, Carlo Fumagalli, Manlio Acquafresca, Mario Moroni, Angela Migliorini, Francesco Meucci, Carlo Di Mario, Niccolò Marchionni, Renato Valenti and Nazario Carrabba
J. Pers. Med. 2026, 16(5), 272; https://doi.org/10.3390/jpm16050272 - 19 May 2026
Viewed by 765
Abstract
Background: Coronary computed tomography angiography (cCTA) is now indicated as a non-invasive tool for ruling out obstructive coronary artery disease (O-CAD) in patients who are candidates for transcatheter aortic valve implantation (TAVI) showing low-intermediate pre-test probability of O-CAD. In elderly and comorbid [...] Read more.
Background: Coronary computed tomography angiography (cCTA) is now indicated as a non-invasive tool for ruling out obstructive coronary artery disease (O-CAD) in patients who are candidates for transcatheter aortic valve implantation (TAVI) showing low-intermediate pre-test probability of O-CAD. In elderly and comorbid TAVI candidates, the safety and accuracy of cCTA as an alternative to invasive coronary angiography (ICA) for ruling out O-CAD remain to be established. Aim: To assess the feasibility, diagnostic accuracy, and clinical safety of cCTA for ruling out proximal O-CAD in elderly, comorbid, high-risk patients undergoing TAVI. Methods: We conducted a retrospective, single-center study including all consecutive patients with severe symptomatic aortic stenosis who underwent TAVI between January 2019 and December 2020. All patients underwent pre-TAVI cCTA. Patients with positive or non-diagnostic cCTA underwent ICA selectively (ICA group). In patients with no-O-CAD, ICA was omitted and proceeded directly to TAVI (no-ICA group). Accordingly, patients were divided into two groups: no-ICA and ICA group. Clinical follow-up was extended up to 5 years, with assessment of major adverse cardiovascular events (MACEs), mortality, heart failure hospitalizations, and unplanned revascularization. Results: Among 355 patients enrolled, 210 were included in the study. Among them, 140 (66.7%) had negative cCTA for O-CAD, and ICA was safely omitted in 132 patients (62.8%). cCTA was inconclusive in 43 patients (20.5%) and positive in 27 (12.9%). ICA confirmed O-CAD in 53 of 78 patients (67.9%) and PCI was performed in 35 of 53 (66.0%). The accuracy of cCTA for ruling in O-CAD was low (66.28%). During the follow-up period (1513 ± 508 days), the no-ICA group showed comparable outcomes to the ICA group in terms of periprocedural complications and long-term results—at both 1 and 5 years—for MACEs, heart failure hospitalizations, mortality and unplanned revascularization. Outcomes remain comparable between the two groups after performing matched-pair analyses. Conclusions: Our data show that cCTA may provide a reliable, safe, and effective alternative to ICA for ruling out obstructive CAD in elderly patients undergoing TAVI when image quality is diagnostic. A cCTA-based strategy allows deferral of ICA in most cases without compromising procedural safety or long-term clinical outcomes, enabling a personalized and tailored clinical pathway. Whether advanced CT techniques, such as CT-FFR and photon-counting CT, may help refine patient selection for invasive coronary assessment remains to be demonstrated. Full article
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