Machine Learning and Artificial Intelligence for Optimizing the Diagnosis and Management of Cardiovascular Disease

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative forces in the field of cardiovascular medicine. The increasing prevalence of cardiovascular disease (CVD) worldwide, coupled with the expansion of electronic health record systems, has created a rich landscape for leveraging large datasets. These resources include demographic details, laboratory values, imaging findings, and outcomes data, all of which can provide clinicians with novel insights into patient risks, disease states, and therapeutic responses. Recent studies in this area have demonstrated the potential of AI and ML to identify undiagnosed conditions, enhance diagnostics using various cardiovascular imaging modalities, and predict short- and long-term outcomes with improved accuracy. Despite these advances, integration into routine clinical practice faces several challenges, including data heterogeneity, reproducibility, interpretability, and establishing clinical benefit in real-world settings. There remains an ongoing debate over the optimal design and validation of AI-based tools, as well as their ethical and regulatory considerations.This Research Topic aims to highlight innovative research and methodologies that leverage AI and ML to address unanswered questions in cardiovascular disease diagnosis and management. Specifically, it seeks to explore how AI-enhanced predictive analytics can fill gaps in clinical decision-making, particularly in scenarios characterized by diagnostic uncertainty or therapeutic equipoise. The focus is on the development, validation, and real-world evaluation of AI-based tools for disease prediction, diagnostic improvement across a range of cardiovascular modalities, and optimization of procedural interventions. By curating original research, reviews, and expert perspectives, this Topic intends to accelerate the safe, effective, and equitable integration of AI technologies into cardiovascular care, ultimately improving outcomes for diverse patient populations.This Research Topic encompasses studies using AI and ML to address clinical uncertainty and optimization in cardiovascular care, with a primary focus on applications grounded in large, established datasets. It welcomes research situated at the intersection of data science and cardiovascular medicine, while recognizing the limitations inherent in retrospective analyses and the need for prospective validation. To gather further insights into the boundaries and opportunities of this field, we welcome articles addressing, but not limited to, the following themes:• Data integration strategies for cardiovascular research and care;• Development and validation of AI/ML models for disease prediction and diagnostics;• Applications of computational modeling and digital twins in cardiovascular disease management;• AI-supported risk stratification and patient selection for invasive procedures;• Assessment of the ethical, regulatory, and practical challenges in clinical AI implementation;• Reviews and perspectives on the current and future landscape of AI/ML in cardiovascular care. Topic editors: Sandeep Saha, Sanjiv Shah, Rahul Aggarwal Source: https://www.frontiersin.org/research-topics/77455/machine-learning-and-artificial-intelligence-for-optimizing-the-diagnosis-and-management-of-cardiovascular-disease