Although artificial intelligence has already shown promise in cardiovascular medicine, most existing tools analyze only one type of data—such as electrocardiograms or cardiac images—limiting their clinical utility. The emergence of multimodal AI, which fuses information from multiple sources, now allows algorithms to mimic the holistic reasoning of cardiologists and deliver more accurate, patient-specific insights.
The review, led by West China Hospital of Sichuan University and the University of Copenhagen, examined more than 150 recent studies. The authors demonstrate that combining complementary modalities—for example, echocardiography with computed tomography, or cardiac magnetic resonance with genomics—significantly boosts diagnostic performance. A transformer-based neural network that merged chest radiographs with clinical variables simultaneously identified 25 critical pathologies in intensive-care patients, achieving an average area-under-the-curve (AUC) of 0.77. In another study, integrating cardiac MRI with genome-wide association data revealed novel genetic loci influencing aortic valve function, opening doors to targeted prevention strategies.
Beyond diagnosis, multimodal AI can refine treatment selection. Machine-learning models that incorporate imaging, laboratory results, and medication history successfully predicted which heart-failure patients would benefit from cardiac resynchronization therapy, distinguishing "super-responders" from non-responders. Similar approaches identified patients unlikely to profit from mitral-valve repair, sparing them unnecessary procedures. The review also reports AI-derived "video biomarkers" extracted from routine echocardiograms that independently forecast the progression of aortic stenosis, enabling opportunistic risk stratification without extra tests.
Continuous, home-based monitoring is another frontier. Algorithms that fuse data from wearables, smartphone apps, and electronic health records can detect early deterioration and deliver automated coaching, potentially reducing hospital readmissions. The authors estimate that widespread adoption of today's multimodal AI could cut cardiovascular healthcare spending by 5%–10% within five years through improved efficiency and fewer complications.
Despite the optimism, the review cautions that data quality, bias, and algorithmic transparency remain major hurdles. Models trained on skewed datasets perform poorly on under-represented ethnic or socioeconomic groups, while the "black-box" nature of deep learning complicates clinical trust. The researchers call for standardized data collection, federated learning platforms, and explainable-AI techniques to accelerate safe translation into routine care.
DOI：https://doi.org/10.1093/pcmedi/pbaf016