What question did this study set out to answer?

To explore the role of artificial intelligence in enhancing diagnosis and treatment in cardiovascular pathology.

April 3, 2026Open Access

Artificial Intelligence in Cardiovascular Pathology: Toward a Diagnostic Revolution

Key Result

Artificial intelligence in cardiovascular pathology improves diagnostic accuracy and reproducibility, but requires robust multicenter validation before routine clinical adoption.

Key Points

To explore the role of artificial intelligence in enhancing diagnosis and treatment in cardiovascular pathology.
Review of AI applications in cardiovascular pathology
Focus on heart transplant rejection, cardiomyopathies, myocarditis, and atherosclerotic diseases
Analysis of multimodal data sources including imaging and histopathology
Discussion of automated methods for diagnostic standardization and quantitative analysis
AI enhances diagnostic accuracy and prognostic stratification in cardiovascular diseases
Improved inter-observer variability through automated approaches
Highlighted the need for large, well-annotated datasets for training AI models
Identified challenges with AI interpretability and ethical considerations in clinical use

Structured PICO

Intervention

Artificial intelligence (AI), including machine learning and deep learning

Artificial intelligence and multimodal data integration hold significant potential to revolutionize cardiovascular pathology diagnostics, but require further validation and standardization.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Artificial intelligence (AI) in cardiovascular pathology involves the use of computational models, including machine learning and deep learning (DL), to analyse complex and heterogeneous data. These data include histopathological whole-slide images, cardiovascular imaging techniques such as cardiac magnetic resonance, echocardiography, computed tomography (CT), clinical parameters, and molecular information. The integration of these multimodal data sources allows AI to overcome the limitations of single-modality analysis, improving diagnostic accuracy, prognostic stratification, and personalised clinical decision-making while reducing inter-observer variability. Cardiovascular disease remains the leading cause of mortality worldwide, highlighting the need for more precise and timely diagnostic tools. AI has shown significant promise, particularly in digital pathology, where the digitisation of histological slides combined with advanced algorithms enables improved diagnosis, prognostic assessment, and translational research. This review summarises current AI applications in cardiovascular pathology, focusing on heart transplant rejection, cardiomyopathies, myocarditis, and atherosclerotic and valvular diseases. Automated methods offer important advantages, including diagnostic standardisation, quantitative histological analysis, and improved reproducibility. However, several challenges remain, such as the need for large, well-annotated shared datasets, limited interpretability of AI models, and ethical and legal issues related to clinical implementation. AI represents a promising tool for advancing cardiovascular pathology and personalised medicine, although robust multicentre validation is required before routine clinical adoption.