The rapid integration of generative artificial intelligence (GenAI) into clinical neuroscience has redefined the paradigms of diagnosis, treatment planning, and patient interaction. This study presents an evidence-based evaluation of GenAI applications across diagnostic and therapeutic domains, with a focus on neurological and neuropsychiatric care. A systematic review of peer-reviewed literature (2019–2025) was conducted following PRISMA guidelines, identifying 142 studies employing large language models (LLMs), diffusion models, and generative adversarial networks (GANs) in clinical contexts. Quantitative synthesis revealed that GenAI-assisted neuroimaging interpretation improved diagnostic accuracy by 17–29% compared with traditional machine learning methods, particularly in Alzheimer’s disease, stroke segmentation, and multiple sclerosis lesion detection. In therapeutic settings, generative models demonstrated significant potential for individualized treatment prediction, neurorehabilitation content generation, and digital twin simulations, enhancing patient engagement and therapeutic adherence.

Furthermore, evidence supports the growing role of LLMs such as GPT-based systems in clinical documentation, differential diagnosis support, and patient education—yielding measurable reductions in clinician workload. However, ethical, interpretability, and data bias concerns remain central challenges, emphasizing the necessity for explainable GenAI frameworks and rigorous clinical validation.

This analysis underscores that, while generative AI is not a replacement for clinician expertise, it acts as a transformative co-pilot—augmenting clinical judgment, reducing diagnostic uncertainty, and personalizing neurological care. Future work should prioritize multimodal data integration, transparent benchmarking, and regulatory harmonization to ensure safe and equitable clinical deployment.