PRONOY JUSTINIANO PEREIRA, ASHLYN EVA PEREIRA
Journal: Medpeer Publisher
ISSN: 3066-2737
Volume: 2
Issue: 12
Date of Publication: 2025/12/10
Bioelectronic medicine is an emerging field that uses targeted electrical stimulation to treat chronic diseases by modulating physiological signals at the cellular and neural levels. However, the complexity and variability of human biological systems present major challenges in ensuring consistent and precise therapeutic outcomes using already available bioelectronic devices. Artificial Intelligence (AI), particularly through advanced algorithmic frameworks, has demonstrated significant potential in overcoming these limitations by enabling dynamic, adaptive, and patient-specific interventions.
This narrative review explores how AI-enhanced algorithms can significantly improve the functionality of closed-loop systems in bioelectronic medicine. It discusses the integration of various deep learning models, real-time data processing techniques, and intelligent control strategies to enhance precision, adaptability, and patient outcomes.
We examined the current literature and technological advances in AI models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, evaluating their applicability in signal interpretation, electrode positioning, and treatment personalization. The review introduces both predictive and non-predictive AI-driven feedback mechanisms used to regulate therapeutic electrical stimulation in chronic conditions. It also outlines system architectures such as multi-level hierarchical control models and embedded AI systems like TinyML, which enable real-time, on-device decision-making with minimal latency.
We found out that AI-powered models allow for the comprehensive mapping of biological systems (e.g., connectomes, electromes, genomes), improving targeting specificity for bioelectronic interventions. Predictive models leverage historical and real-time patient data to adjust stimulation parameters proactively, while non-predictive models enable immediate response to dynamic physiological changes. Hierarchical AI architectures offer multi-tasking capabilities essential for long-term, adaptive treatment. Embedded AI systems and edge computing facilitate real-time diagnostics and remote monitoring, enhancing telemedicine applications and patient compliance, particularly in managing complex chronic diseases such as epilepsy, diabetes, and cardiovascular disorders.
In summary, our article suggests that the integration of AI into bioelectronic medicine has the potential to revolutionize chronic disease management through highly individualized, responsive, and scalable treatment modalities. This review highlights how AI-driven enhancements can transform traditional closed-loop systems into intelligent therapeutic platforms. However, ethical concerns regarding data privacy, bias, accountability, and regulatory oversight remain significant and warrant further investigation. Advancing this synergy between AI and bioelectric systems could open new frontiers in precision medicine and healthcare accessibility.
connectomes, deep learning (dl), electroceuticals, electromes, machine learning (ml)
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