REVIEW article
Front. Cardiovasc. Med.
Sec. Atherosclerosis and Vascular Medicine
"Artificial Intelligence in Atherosclerosis: Enhancing Prediction and Personalised Management"
Makhabbat Bekbossynova 1
Timur Saliev 2
Tatyana Ivanova-Razumova 1
Saltanat Andossova 1
Aknur Kali 1
Gulzhan Myrzakhmetova 1
1. University Medical Center Corporate Fund, Astana, Kazakhstan
2. Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
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Abstract
Atherosclerosis remains a leading global cause of cardiovascular morbidity and mortality. As the disease's clinical manifestations are often delayed and its progression is influenced by a multitude of genetic, metabolic, and environmental factors, early risk assessment and personalised management are essential. Traditional tools for risk prediction and disease monitoring, while useful, have limitations in accuracy and adaptability. Recent advances in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), hold transformative potential for predicting, diagnosing, and individualising the treatment of atherosclerosis. This review explores how AI-driven models enhance cardiovascular risk stratification, automate and improve plaque characterisation via advanced imaging analysis, and enable precision treatment planning tailored to each patient's unique profile. We also examine the ethical implications, data-related challenges, and future directions needed to support the clinical adoption of AI in atherosclerosis care. With proper integration and oversight, AI holds the promise of reshaping cardiovascular medicine by making it more predictive, preventive, and personalised.
Summary
Keywords
Atherosclerosis, artificial intelligence, machine learning, risk stratification, Personalised medicine, cardiovascular disease, imaging analysis, predictive modelling
Received
26 June 2025
Accepted
12 December 2025
Copyright
© 2025 Bekbossynova, Saliev, Ivanova-Razumova, Andossova, Kali and Myrzakhmetova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Timur Saliev
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.