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The Use of Artificial Intelligence in Cardiovascular Medicine

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Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative tool in cardiovascular medicine, offering unprecedented opportunities to enhance diagnostic accuracy, optimize treatment strategies, and improve patient outcomes. This Special Issue, entitled The Use of Artificial Intelligence in Cardiovascular Medicine, aims to explore the integration of AI technologies across diverse domains of cardiovascular health. We aim to focus on state-of-the-art advancements, including AI-driven imaging techniques, ECG analysis, predictive analytics for cardiovascular risk, machine learning algorithms for arrhythmia detection, and AI applications in personalized medicine. Additionally, we seek to address challenges such as data privacy, ethical considerations, and the validation of AI tools in clinical practice. By bringing together multidisciplinary perspectives, this Special Issue aspires to provide a comprehensive overview of the current and future impact of AI on cardiovascular care.

Dr. Stefano Palermi
Guest Editor

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39 pages, 650 KB

Open AccessReview

Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review

by Cagri Ayhan, Marina Mekhaeil, Rita Channawi, Alp Eren Ozcan, Elif Akargul, Atakan Deger, Incilay Cayan, Amr Abdalla, Christopher Chan, Ronan Mahon, Dilara Ayhan, William Wijns, Sherif Sultan and Osama Soliman

J. Clin. Med. 2025, 14(23), 8420; https://doi.org/10.3390/jcm14238420 - 27 Nov 2025

Cited by 7 | Viewed by 2297

Abstract

Acute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today’s data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models—particularly ensemble algorithms and biomarker-integrated approaches—have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI’s growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics—enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia—showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling—potentially through the development of digital twins—will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA. Full article

(This article belongs to the Special Issue The Use of Artificial Intelligence in Cardiovascular Medicine)

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