Artificial Intelligence and Machine Learning inNeuroregeneration: A Systematic Review

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains,including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience andneurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, andsurgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervoussystem's complexities. However, research on AI/ML in neuroregeneration is fragmented,necessitating a comprehensive review. Adhering to Preferred Reporting Items for SystematicReviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing onAI/ML in neuroregeneration were selected from a total of 247. Two researchers independentlyconducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT)2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals includeddiagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12%each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronichealth records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard MLalgorithms constituted 29%. The review underscores the growing interest in AI/ML forneuroregenerative medicine, with increasing publications. These technologies aid in diagnosingdiseases and facilitating functional recovery through robotics and targeted stimulation. AI-drivendrug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressingexisting limitations remains crucial in this rapidly evolving field.