Artificial intelligence and machine learning approaches offer transformative opportunities to map, model, and predict brain-heart network dynamics for personalized risk stratification.
The intricate interplay between the brain and heart underpins both physiological regulation and pathophysiological processes, yet decoding these interactions remains a formidable challenge. Recent advances in artificial intelligence (AI) offer transformative opportunities to map, model, and predict brain-heart network dynamics with unprecedented precision. This review synthesizes current knowledge on AI approaches applied to neurocardiology, encompassing multimodal data integration from neuroimaging, electrophysiology, autonomic signals, and cardiovascular monitoring. We examine machine learning and deep learning strategies for identifying biomarkers, forecasting adverse cardiac events, and elucidating mechanisms linking neurological, psychiatric, and cardiovascular disorders. Clinical applications are explored across heart failure, arrhythmias, stroke-induced cardiac dysfunction, epilepsy, and stress-related conditions, highlighting AI's potential for personalized risk stratification. The role of wearable devices, digital phenotyping, and real-world data collection in continuous brain-heart monitoring is discussed, alongside AI-enabled early warning systems. Critical considerations regarding data quality, bias, interpretability, privacy, and ethical governance are emphasized to guide responsible deployment. Finally, we outline emerging directions, including integrative digital twins, federated AI, and closed-loop neuromodulation. By bridging computational innovation and clinical neuroscience, AI-driven approaches promise to redefine neurocardiology, offering predictive, mechanistic, and therapeutic insights into the brain-heart axis.
Varzideh et al. (Mon,) conducted a review in Neurocardiology and brain-heart interactions. Artificial Intelligence was evaluated. Artificial intelligence and machine learning approaches offer transformative opportunities to map, model, and predict brain-heart network dynamics for personalized risk stratification.