{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T14:02:35Z","timestamp":1791295355395,"version":"4.1.0"},"reference-count":70,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Arrhythmia"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Cardiac neuromodulation includes various methods, such as vagus nerve stimulation, baroreflex activation therapy, renal denervation, and stellate ganglion intervention, and targets the autonomic imbalance contributing to the pathophysiology of many cardiovascular diseases. Despite promising mechanistic evidence, several landmark trials, including INOVATE\u2010HF, NECTAR\u2010HF, and SYMPLICITY HTN\u20103, did not meet their primary clinical outcomes, with substantial numbers of non\u2010responders observed across therapies. Variation in patient response is attributed to several unresolved issues, including insufficient stimulation dosing, off\u2010target or non\u2010selective fiber activation, and differences in autonomic phenotypes between patients. Both problems highlight the need for individualized approaches to patient selection, therapy delivery, and monitoring. Artificial intelligence (AI) offers tools to address these problems. In this narrative review, we describe seven families of AI techniques relevant to cardiac neuromodulation: supervised machine learning, deep learning, representation learning, reinforcement learning, multimodal fusion, digital twins with physics\u2010informed AI, and explainable AI with federated learning. For each family, we summarize how the method works, the cardiac neuromodulation problem it addresses, and the available evidence in the field of cardiac electrophysiology. We then map these techniques to the three core problems of patient selection, real\u2010time stimulation control, and longitudinal response monitoring. The strongest evidence to date supports representation learning for VNS responder identification, reinforcement learning for closed\u2010loop VNS control, and digital twins for in silico testing of stimulation protocols. The opportunity for the field is to translate these methods, most of which were developed in adjacent fields, into prospective cardiac neuromodulation trials.<\/jats:p>","DOI":"10.1002\/joa3.70434","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T07:37:52Z","timestamp":1784878672000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9263-5989","authenticated-orcid":false,"given":"Kshitiz","family":"Pandey","sequence":"first","affiliation":[{"name":"College of Medical Sciences Teaching Hospital  Chitwan 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