{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T18:14:52Z","timestamp":1784916892740,"version":"3.55.0"},"reference-count":200,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:p>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\u2013heart 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\u2019s potential for personalized risk stratification. The role of wearable devices, digital phenotyping, and real-world data collection in continuous brain\u2013heart 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\u2013heart axis.<\/jats:p>","DOI":"10.3389\/fnins.2026.1788653","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T05:25:55Z","timestamp":1774848355000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence in neurocardiology: decoding brain\u2013heart network interactions for clinical and translational insights"],"prefix":"10.3389","volume":"20","author":[{"given":"Fahimeh","family":"Varzideh","sequence":"first","affiliation":[{"name":"Department of Molecular, Cellular and Biomedical Sciences, School of Medicine, City University of New York","place":["Manhattan, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivangi","family":"Pande","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cellular and Biomedical Sciences, School of Medicine, City University of New York","place":["Manhattan, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislovas S.","family":"Jankauskas","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cellular and Biomedical Sciences, School of Medicine, City University of New York","place":["Manhattan, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pasquale","family":"Mone","sequence":"additional","affiliation":[{"name":"Casa di Cura \u201cClinica Montevergine\u201d","place":["Mercogliano, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Urna","family":"Kansakar","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cellular and Biomedical Sciences, School of Medicine, City University of New York","place":["Manhattan, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaetano","family":"Santulli","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cellular and Biomedical Sciences, School of Medicine, City University of New York","place":["Manhattan, NY, United States"]},{"name":"International Translational Research and Medical Education (ITME) Consortium, Joint Academic Research Unit","place":["Naples, Italy"]},{"name":"Departments of Medicine (Cardiology) and Molecular Pharmacology, Albert Einstein College of Medicine, Wilf Family Cardiovascular Research Institute, Einstein Institute for Neuroimmunology and Inflammation (INI), Fleischer Institute for Diabetes and Metabolism (FIDAM)","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.3390\/healthcare13172154","article-title":"Explainable AI in clinical decision support systems: A meta-analysis of methods, applications, and usability challenges.","volume":"13","author":"Abbas","year":"2025","journal-title":"Healthcare"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e73846","DOI":"10.2196\/73846","article-title":"AI-driven real-time monitoring of cardiovascular conditions with wearable devices: Scoping review.","volume":"13","author":"Abedi","year":"2025","journal-title":"JMIR Mhealth. 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