Multimodal AI to Revolutionize Cardiovascular Disease Diagnosis and Treatment

Cardiovascular diseases remain the leading cause of death worldwide, and while artificial intelligence (AI) has shown promise in their diagnosis and management, most existing systems rely on a single data type such as ECGs or cardiac imaging. This limits diagnostic accuracy and prevents algorithms from reflecting the comprehensive reasoning process that physicians use in clinical practice. Now, a new multimodal AI approach aims to overcome this limitation by integrating diverse clinical data sources to deliver more accurate and personalized cardiovascular insights.

The study, led by West China Hospital of Sichuan University (Sichuan, China) and the University of Copenhagen (Copenhagen, Denmark), reviewed over 150 studies demonstrating the potential of multimodal AI in cardiovascular medicine. This next-generation approach fuses complementary data modalities—such as echocardiography, computed tomography, magnetic resonance imaging, and genomics—to enhance diagnostic precision. For example, a transformer-based neural network combining chest radiographs with clinical variables simultaneously identified 25 critical pathologies in intensive-care patients, achieving an average diagnostic accuracy (AUC) of 0.77.

Image: Preprocessing, building, and applying multimodal AI in cardiology (Xin-yue Yang et al., Precision Clinical Medicine (2025). DOI: 10.1093/pcmedi/pbaf016)

The review, published in Precision Clinical Medicine, showed that multimodal AI can also reveal new biological insights. By integrating cardiac MRI with genome-wide association data, researchers identified novel genetic loci linked to aortic valve function. In addition to diagnosis, these models refined treatment decisions—predicting which heart-failure patients would respond to cardiac resynchronization therapy and identifying those unlikely to benefit from mitral-valve repair, thereby improving patient selection and treatment efficiency.

Emerging multimodal algorithms also enable continuous health monitoring by merging data from wearable devices, mobile applications, and electronic health records. These tools can detect early signs of deterioration, deliver automated health coaching, and reduce readmission rates. The authors estimate that adopting multimodal AI in clinical practice could reduce cardiovascular healthcare costs by 5%−10% over five years through improved efficiency and fewer complications.