Seven families of AI techniques relevant to cardiac neuromodulation are described: supervised machine learning, deep learning, representation learning, reinforcement learning, multimodal fusion, digital twins with physics‐informed AI, and explainable AI with federated learning.
Abstract
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‐HF, NECTAR‐HF, and SYMPLICITY HTN‐3, did not meet their primary clinical outcomes, with substantial numbers of non‐responders observed across therapies. Variation in patient response is attributed to several unresolved issues, including insufficient stimulation dosing, off‐target or non‐selective 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‐informed 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‐time stimulation control, and longitudinal response monitoring. The strongest evidence to date supports representation learning for VNS responder identification, reinforcement learning for closed‐loop 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.
This narrative review article uniquely integrates current and emerging applications of AI across full SCS pathway while also further critically highlighting evidence gaps, future directions for precision neuromodulation.
Chitra Kolla, Sheetal K. Madavi, Souvik Banik et al.· Journal of Clinical and Diag...· 0 citations
Major depressive disorder remains a leading cause of global disability, with a substantial fraction of patients exhibiting inadequate response to conventional pharmacological and psychotherapeutic treatments. Vagus nerve stimulation has emerged as an effective neuromodulation strategy for treatment-resistant depression, yet implantable systems are limited by surgical risk, cost, and restricted scalability. Recent advances in non-invasive, wearable vagus nerve stimulation technologies, particularly transcutaneous auricular and cervical approaches, offer a promising pathway toward accessible, home-based neuromodulation. This Review synthesizes the anatomical and physiological rationale underlying wearable non-invasive VNS and critically evaluates mechanistic evidence spanning monoaminergic modulation, neuroplasticity, neuroimmune regulation, autonomic control, and large-scale brain network reorganization. We further summarize clinical findings from implantable and non-invasive trials and discuss key engineering considerations, including electrode–skin interfaces, stimulation parameters, wearability, and safety. Finally, we highlight major challenges and future opportunities, emphasizing the integration of flexible bioelectronics, multimodal sensing, and Artificial Intelligence-driven closed-loop control to enable personalized, scalable neuromodulation for depression. Reviews vagus nerve stimulation mechanisms in depression, including autonomic, inflammatory, and neural pathways. Compares major vagus nerve stimulation modalities, with emphasis on transcutaneous auricular and cervical approaches. Analyzes device design, including electrode configuration, stimulation parameters, and ergonomic considerations. Summarizes clinical evidence on the efficacy, safety, and limitations of noninvasive vagunerve stimulation. Identifies challenges in standardization and personalization and proposes AI-assisted, closed-loop approaches. Reviews vagus nerve stimulation mechanisms in depression, including autonomic, inflammatory, and neural pathways. Compares major vagus nerve stimulation modalities, with emphasis on transcutaneous auricular and cervical approaches. Analyzes device design, including electrode configuration, stimulation parameters, and ergonomic considerations. Summarizes clinical evidence on the efficacy, safety, and limitations of noninvasive vagunerve stimulation. Identifies challenges in standardization and personalization and proposes AI-assisted, closed-loop approaches.
Vivian Wei, Xiaofeng Chen, Jiayi Li et al.· Med-X· 0 citations
A multicenter crossover study is proposed for adults with levodopa-responsive Parkinson’s disease and motor fluctuations despite optimized conventional stimulation, which combines blinded motor-state improvement with reduced troublesome dyskinesia and off time.
Background The interpretation of pediatric electrocardiograms (ECGs) and management of childhood arrhythmias represent specialized clinical disciplines complicated by age-dependent physiological evolution. While artificial intelligence (AI) has transformed adult cardiology, its application to pediatric electrophysiology remains largely in the research phase. Objective This review critically appraises the current evidence, methodological rigor, clinical readiness, and translational challenges of AI in pediatric arrhythmia detection, risk stratification, and management. Methods A synthesis of contemporary literature was conducted, evaluating machine learning (ML), deep learning (DL), large language models (LLMs), wearable sensors, and intensive care monitoring across pediatric cohorts. Main findings While purpose-built DL models demonstrate strong diagnostic performance for specific electrical phenotypes—such as Wolff-Parkinson-White (WPW) syndrome, long QT syndrome (LQTS), and neonatal bradycardia—the vast majority of published tools remain unvalidated retrospective proofs-of-concept. Current AI algorithms analyze isolated ECG waveforms under curated conditions and cannot replace holistic clinical evaluations incorporating patient history, family screening, genetics, and multi-modality diagnostic testing. Significant barriers persist, including pervasive data scarcity, lack of prospective external validation, limited saliency map reproducibility in Explainable AI (XAI), and uncalibrated false alarms. Furthermore, adult-trained algorithms and general-purpose LLMs yield unacceptable diagnostic error rates when applied to children. Conclusions AI holds promise for enhancing pediatric arrhythmia care, but clinical integration requires moving beyond isolated performance metrics. Future progress hinges on prospective multicenter validation, privacy-preserving federated learning, multimodal data integration, and explicit definition of AI's role as a clinical decision-support tool within real-world workflows.
Hailin Jia, Wenjing Zhu, J. Lv· Frontiers in Pediatrics· 0 citations
Future progress in SCS will likely depend on artificial intelligence, remote monitoring, biomarker-guided programming, and integration with multidisciplinary chronic pain care.
Nafay Abdul, Milan Patel, Rohit Aiyer et al.· Journal of Clinical Medicine· 0 citations
This review provides a comprehensive synthesis of TI's mechanistic foundations, safety profiles, and therapeutic trajectory, while critically discussing the integration of closed-loop systems, multi-target paradigms, and patient-specific optimization as the next frontiers in non-invasive deep brain stimulation.
