Cardiovascular diseases are significant contributors to both global morbidity and mortality, highlighting the pressing need for enhanced risk assessment techniques. The rapid development of artificial intelligence (AI) algorithms presents an opportunity to transform this critical area. Utilizing machine learning and deep learning, AI has the capacity to process extensive and varied medical data—from genetic information to clinical records and imaging—allowing for more precise identification of risk factors and earlier predictions of disease onset. These advances could lead to timelier interventions and improved outcomes for patients, addressing a vital need in cardiovascular healthcare.
This Research Topic seeks to gather the latest advancements in the application of AI algorithms and precision medicine for assessing the risk of cardiovascular disease. It aims to present innovative methods, personalized treatment, confirm the efficacy of current technologies, and investigate new challenges and future directions within the field. This effort is meant to encourage the integration of AI into clinical practice, ultimately improving the personalized management of cardiovascular health.
To deepen our understanding of AI’s role in cardiology, we invite contributions that focus on, but are not limited to, the following areas:
o Development and validation of AI models for predicting cardiovascular disease risks
o Development and validation of AI models aim to improve the precision and efficacy of cardiovascular diseases
o Integration of various data sources, such as electronic health records and genomic data, into AI algorithms
o Comparative studies of personalized AI-driven techniques for risk assessment
o Clinical implications and uses of AI and precision medicine in cardiovascular medicine
o Comparative analyses of the performance of AI models in risk stratification
These topics are integral for advancing the application of AI in cardiovascular care, aiming to enhance how risks are assessed and managed in the field.
Keywords: Cardiovascular Disease, Risk Assessment, Artificial Intelligence, Machine Learning, Deep Learning
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
