Journal of Cardiology and Heart Failure

ABSTRACT

Background

Cardiovascular disease (CVD) remains the leading cause of global mortality, accounting for nearly 20 million deaths annually and placing a profound clinical and economic strain on healthcare infrastructure. Despite major advances in pharmacological therapies, structural interventions, and cardiac imaging, myocardial decompensation frequently develops silently before clinical symptoms emerge. Traditional diagnostic paradigms primarily rely on isolated biomarkers, episodic imaging, and subjective interpretation, often failing to capture the complex, multi-system biological interactions that precede overt clinical deterioration. Multimodal artificial intelligence (AI) has emerged as a promising analytical approach to integrate disparate biomedical data streams for earlier risk stratification.

Scientific Objectives

This narrative review presents a translational conceptual framework detailing the integration of multimodal AI across the continuum of cardiovascular care. Specifically, we examine how cross-modal data fusion may enhance early detection of subclinical myocardial decompensation, refine precision pharmacotherapy, and inform prospective strategies for cardiac recovery, while explicitly delineating established clinical evidence from speculative translational hypotheses.

Methodology & Conceptual Framework Construction

We executed a cross-disciplinary narrative synthesis combining clinical cardiology, computational systems biology, biomedical informatics, and deep learning architectures. Heterogeneous biomedical inputs, including electronic health records, continuous electrophysiological waveforms, multi-parametric functional imaging, ambulatory telemetry, laboratory biomarkers, multi-omics, and socio-environmental determinants, were evaluated for cross-modal alignment and clinical utility. Emerging computational paradigms (high-parameter foundation models, mechanistic digital twins, federated learning networks, and explainable AI) were critically evaluated regarding their current evidence maturity, data harmonization challenges, and integration into point-of-care workflows.

Results Synthesized Framework & Key Insights

Multimodal AI provides a robust framework for harmonizing complex biomedical datasets, offering measurable improvements in diagnostic discrimination and early risk prediction over single-modality approaches. However, a critical distinction must be maintained between prognostic prediction and causal therapeutic benefit. While AI models demonstrate high accuracy in identifying latent disease trajectories, prospective evidence that algorithmic recommendations directly prevent decompensation, drive reverse ventricular remodeling, or accelerate tissue healing remains limited and requires rigorous validation through randomized clinical trials.

Clinical Implications

Integrating multimodal AI into cardiovascular practice represents a transitional shift toward predictive and personalized care. To achieve meaningful clinical utility, AI-enabled decision support systems must prioritize algorithmic calibration, temporal validation, transparent explainability, and human-in-the-loop governance to mitigate automation bias, ensure equitable performance, and optimize resource utilization.

Keywords: Artificial Intelligence; Multimodal AI; Cardiovascular Medicine; Precision Cardiology; Heart Failure; Myocardial Decompensation; Machine Learning; Deep Learning; Clinical Decision Support Systems; Digital Health; Cardiovascular Digital Twins; Multi-Omics Integration; Systems Biology; Polygenic Risk Scores; Pharmacogenomics; Explainable AI; Federated Learning; Learning Health Systems; Predictive Analytics; Myocardial Remodeling

Jel Classification: I11; I12; I18; O33; I15; O31, I15; O31; C88; D83