arXiv:2601.12699v2 Announce Type: replace Abstract: Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life and cause side effects while failing to adapt to changing neural dynamics. Recent reinforcement learning approaches improve adaptability, yet most rely on deep neural networks that require offline training and are computationally too expensive for implantable hardware. This paper presents a resource-conscious adaptive DBS framework based on a Time- and Threshold-Triggered Pruned Multi-Armed Bandi
Advances in machine learning, particularly 'resource-conscious' algorithms, are making real-time adaptive medical devices feasible for implantable hardware, addressing previous computational and energy limitations.
This development represents a significant step towards more effective, adaptive, and patient-friendly neurostimulation therapies, reducing side effects and extending device longevity.
The shift from fixed-parameter to adaptive, AI-driven Deep Brain Stimulation allows for more personalized and dynamic treatment, enhancing both efficacy and patient quality of life.
- · Medical device manufacturers
- · Parkinson's disease patients
- · Neuroscience researchers
- · AI algorithm developers
- · Developers of computationally intensive, non-adaptive neural network solutions f
Adaptive DBS becomes more widespread, improving outcomes for neurological disorders.
The precedent set by resource-conscious AI in DBS encourages its application in other implantable medical devices and bio-integrated systems.
Increased patient reliance on AI-driven implantable devices raises new ethical and regulatory questions around autonomous medical decision-making within the body.
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