Presentation (CTI)

OBJECTIVES: To systematically review the latest technological advancements in artificial intelligence (AI)-integrated electroceuticals for neurotransmitter dysregulation disorders and structure their clinical application value into core clinical functional domains.
METHODS: A narrative literature review was conducted using MEDLINE, Embase, Cochrane Library, and KoreaMed databases. Peer-reviewed human studies published since 2013 that applied AI-based electroceutical technologies to neurotransmitter-related disorders, including Parkinson's disease, major depressive disorder, and epilepsy, were selected for qualitative analysis.
RESULTS: Twenty-three studies met the inclusion criteria, mostly from the United States (n=7) and China (n=5). Target conditions primarily included Parkinson's disease (70%, n=16), major depressive disorder(17%, n=4), and epilepsy(4%, n=1), with deep brain stimulation being the primary modality(82%, n=19). The clinical roles of AI-integrated electroceuticals can be structured into three domains. First, multimodal biomarkers such as neuroimaging and biometrics are utilized to proactively identify optimal candidates and predict individualized treatment suitability. Second, treatment protocols are optimized through closed-loop control systems that dynamically adapt stimulation parameters in real-time based on patient-specific neurophysiological states, such as local field potentials. Third, the integration of continuous neuro-signals with wearable behavioral data establishes a monitoring system for tracking long-term symptom progression and anticipating acute exacerbations. Most studies remained at the prospective feasibility stage with small sample sizes.
CONCLUSIONS: AI is driving a paradigm shift, transitioning electroceuticals from pre-programmed, static open-loop devices into dynamic, personalized precision therapy platforms. The identified clinical roles enhance the understanding of the clinical integration of intelligent electroceuticals and serve as a foundational academic resource for future technology assessments and follow-up research.