Integrating Artificial Intelligence in the Pathology of Major Cardiovascular Diseases: From Atherosclerosis to Heart Failure

Abstract

The growing burden of cardiovascular diseases (CVDs) highlights the need for more accurate, efficient, and standardized diagnostic and prognostic approaches. This review aims to explore the integration of artificial intelligence (AI) in the pathology of major CVDs, emphasizing its potential to enhance diagnostic precision, improve patient risk stratification, and support clinical decision-making. By evaluating recent advancements, this review underscores the transformative impact of AI in cardiovascular pathology and its role in shaping future healthcare practices. AI-powered algorithms, particularly machine learning and deep learning, have significantly improved the accuracy of CVD diagnosis by detecting subtle pathological changes in histopathology slides, imaging data, and molecular profiles. In cardiovascular imaging, AI enhances image interpretation, enabling precise detection of atherosclerotic plaques, myocardial fibrosis, and valvular abnormalities, reducing inter-observer variability. AI-based predictive models are revolutionizing risk stratification by analyzing patient-specific data, aiding in early disease detection and personalized treatment plans. The review also highlights AI’s role in drug discovery and clinical trials, where it accelerates candidate selection, optimizes trial design, and personalizes therapeutic interventions. Despite its potential, the widespread adoption of AI in CVD pathology faces challenges, including data standardization issues, algorithm interpretability, and the need for clinical validation. Future research should focus on developing more transparent and interpretable AI models to foster trust among clinicians and ensure their seamless integration into routine cardiovascular care. The incorporation of multimodal AI systems, combining imaging, genomic, and clinical data, holds promises for creating highly personalized diagnostic and therapeutic strategies. Continuous collaboration between AI developers, pathologists, and clinicians will be essential to address technical challenges, ensure ethical deployment, and maximize AI’s clinical utility in CVD pathology.