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Abstract Deep brain stimulation (DBS) is a highly effective treatment for movement disorders like Parkinson's disease, essential tremor, and dystonia. However, the current multidisciplinary workflow for implanting and programming DBS is often complex, which can lead to improperly placed leads, suboptimal symptom management, and increased procedure time, ultimately resulting in poor patient outcomes. There is a pressing need for a more streamlined, accurate, reproducible, and personalized approach to DBS therapy. Artificial intelligence (AI), which can analyze complex data and identify patterns with remarkable speed, holds significant promise as a tool to address these challenges. This narrative review explores the current and future applications of AI in improving the entire DBS workflow, from surgical planning and lead placement to postoperative programming, with the goal of enhancing clinical efficiency and achieving better, more personalized outcomes for patients with movement disorders.
Mehta et al. (Tue,) studied this question.
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