Artificial Intelligence in Spinal Cord Stimulation and Neuromodulation: A Narrative Review of Clinical Applications, Emerging Evidence, and Future Directions
Sep 2026· Journal of Clinical and Diagnostic Research· 0 citations
TL;DR
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.
Abstract
Spinal Cord Stimulation (SCS) is known as an established neuromodulatory therapy which is used for refractory chronic pain; however, its clinical outcomes remain heterogeneous due to limitations in patient selection, subjective outcome assessment, and trial-and-error programming strategies. Recent advances into Artificial Intelligence (AI) as well as Machine Learning (ML) have further introduced data-driven approaches for addressing such challenges through leveraging high-dimensional clinical, electrophysiological, imaging, and patient-reported datasets. The present narrative review article summarises emerging role of AI into SCS and neuromodulation, which is focused on AI-assisted selection of patients, intelligent programming, closed-loop adaptive systems, as well as clinical evidence. AI techniques which are inclusive of supervised and unsupervised learning, Deep Learning (DL), and Reinforcement Learning (RL), enable improved prediction of responders, phenotyping of chronic pain populations, and realtime optimisation of stimulation parameters. Early clinical and pilot studies suggest promising improvements into personalisation, therapeutic consistency; however, evidence still remains limited by small sample sizes, heterogeneity, also lack of large prospective trials. Ethical, regulatory, data-governance challenges like privacy, algorithmic bias, transparency, accountability further act as additional barriers for widespread adoption of AI into SCS as well as neuromodulation. Future research must focus on validation at multicenter-level, standardised-type of data frameworks, explainable AI along with adaptive regulatory pathways. AI-based SCS thus holds very significant potential into advancement of precision neuromodulation, provided responsible integration, rigorous clinical validation are adequately achieved. The current 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.
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.
Kshitiz Pandey, Pratik Pandey, Sushmita Khanal et al.· Journal of Arrhythmia· 0 citations
Chronic pain continues to be a major global health burden and is frequently refractory to conventional pharmacologic and conservative therapies. Spinal cord stimulation (SCS) has emerged as an important neuromodulatory treatment for selected patients with chronic neuropathic and mixed pain syndromes. Since its introduction in the 1960s, SCS has evolved from paresthesia-based tonic stimulation into more adaptive and personalized neuromodulation. This review summarizes the current evidence regarding the mechanisms, clinical applications, technological advances, and future directions of SCS therapy. Mechanistically, SCS modulates nociceptive transmission through dorsal column and dorsal horn pathways, inhibitory neurotransmitter systems, wide-dynamic-range neuronal activity, and supraspinal pain-processing networks. Technological advances have expanded available stimulation paradigms, including burst stimulation, high-frequency stimulation, closed-loop evoked compound action potential-controlled systems, and differential target multiplexed stimulation. These approaches aim to improve analgesic durability, reduce the burden of paresthesia, and address mechanisms such as neuroinflammation and neural habituation. Clinically, SCS is used for conditions including failed back surgery syndrome, complex regional pain syndrome, painful diabetic neuropathy, ischemic limb pain, and emerging non-traditional pain states. However, outcomes remain variable and are influenced by psychological readiness, pain phenotype, anatomic factors, trial response, neurophysiologic markers, and patient engagement. Complications such as lead migration, infection, implantable pulse generator malfunction, and loss of efficacy remain important considerations. 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
Invasive spinal cord stimulation (SCS) is used in selected patients with refractory chronic neuropathic pain and has also been explored in spinal cord injury, disorders of consciousness, and Parkinson’s disease. However, the strength of evidence differs substantially across indications. This review summarizes the current clinical applications of invasive SCS and distinguishes relatively established pain indications from emerging neurological applications.
We conducted a structured search of PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, Wanfang, and China National Knowledge Infrastructure. The search was supplemented by reference tracking for landmark studies and a targeted update of relevant publications from 2024 to 2026.
Randomized evidence is concentrated in chronic pain populations. Conventional SCS has shown benefit in selected patients with failed back surgery syndrome, while 10-kHz SCS produced greater pain reduction than conventional low-frequency stimulation in patients with chronic back and leg pain. Burst stimulation was noninferior to tonic stimulation in the SUNBURST trial and was preferred by most participants. Randomized evidence also supports 10-kHz SCS in refractory painful diabetic neuropathy. Evidence for complex regional pain syndrome suggests short- to intermediate-term pain relief, although long-term benefit remains uncertain. By contrast, applications in diabetic foot complications, spinal cord injury, and disorders of consciousness are supported mainly by small, heterogeneous, or uncontrolled studies. Recent blinded trials have not demonstrated a clear benefit of SCS for Parkinsonian gait impairment.
The clinical evidence supporting invasive SCS varies considerably across neurological indications. Its role is comparatively well supported in selected chronic neuropathic pain populations, particularly chronic back and leg pain after spinal surgery and painful diabetic neuropathy. Evidence for non-pain neurological disorders remains limited, and these applications should remain investigational until further controlled studies clarify patient selection, stimulation parameters, durability of response, and safety.
Mengyun He, Yu Tan, Ze He et al.· Frontiers in Neurology· 0 citations
The available evidence suggests that continued multidisciplinary collaboration and technological innovation will facilitate the progressive integration of BCIs into routine neurosurgical care, supporting personalized therapeutic strategies aimed at improving functional recovery, communication, and quality of life in patients with complex neurological disorders.
Alejandra Mendoza Ortiz, Marco Antonio, Ortiz Ayala et al.· International science journa...· 0 citations
ABSTRACT
Deep brain stimulation (DBS) has emerged as a cornerstone therapy for people with Parkinson's disease (PD) who experience motor fluctuations and dyskinesias refractory to optimized medical treatment. This narrative review provides a concise, clinically oriented overview of DBS tailored for neurologists, residents, and trainees involved in the evaluation and longitudinal management of advanced PD. The review discusses the evolution of DBS, contemporary patient selection criteria, approved stimulation targets, including the subthalamic nucleus, globus pallidus interna, and ventral intermediate nucleus of the thalamus, and the multidisciplinary presurgical workup. Practical aspects of surgery, device programming strategies, and expected clinical outcomes are highlighted, alongside an evidence-based discussion of risks, complications, and comparative effectiveness relative to other device-aided therapies. The central role of the neurologist, from early referral to long-term device management, is emphasized, as are recent advances such as directional leads, adaptive DBS, and sensing-enabled systems that are reshaping the therapeutic landscape. This review aims to equip neurologists with essential knowledge and decision-making tools to safely and effectively integrate DBS into clinical practice.
Ranjot Kaur, Divya M Radhakrishnan, Roopa Rajan et al.· Annals of Indian Academy of...· 0 citations
AbstractBackground. Conventional deep brain stimulation provides continuous therapy for Parkinson’s disease, but fixed stimulation cannot accommodate medication cycles, sleep–wake transitions, gait freezing, dyskinesia, or biomarker drift. Adaptive deep brain stimulation offers closed- loop neuromodulation by adjusting stimulation according to sensed neural or behavioral signals.Materials and methods. A structured narrative review used randomized and nonrandomized trials, prospective cohorts, documents, neurophysiological studies, and investigations published through July 2026. Evidence was organized by biomarker validity, control architecture, programming feasibility, effectiveness, safety, energy efficiency, generalizability, and human-factor integration. A multicenter crossover study is proposed for adults with levodopa-responsive Parkinson’s disease and motor fluctuations despite optimized conventional stimulation.Results. Subthalamic beta amplitude and beta-burst duration remain the most mature control variables, whereas stimulation-entrained gamma activity, cortical signals, wearable-derived gait events, and multimodal decoders may better capture dyskinesia, freezing, and naturalistic behavior. Chronic studies suggest that personalized adaptive stimulation can improve residual motor symptoms and quality of life, while gait-synchronized and activity-dependent paradigms may address axial disability. Major limitations include sensing artifacts, unstable biomarkers, heterogeneous programming, small samples, insufficient blinding, and limited evidence regarding cognition, speech, falls, and device burden. The proposed primary endpoint combines blinded motor-state improvement with reduced troublesome dyskinesia and off time. Secondary endpoints include falls, gait freezing, speech, cognition, quality of life, stimulation energy, programming time, adverse events, calibration, and subgroup performance.Conclusion. Adaptive deep brain stimulation is transitioning from experimental physiology to regulated clinical therapy. Its durable value will depend on biomarker personalization, transparent algorithms, standardized outcomes, and independent multicenter validation.Keywords: Parkinson’s disease, adaptive deep brain stimulation, closed-loop neuromodulation, beta oscillations, local field potentials, gait freezing, neural biomarkers, personalized neurostimulation.