Neural Networks for Understanding Neurological Disorders: A Systematic Review

Abstract Artificial intelligence (AI) is increasingly being integrated into neurology for early diagnosis, effective monitoring, and accurate therapeutic decision‑making. AI‑assisted analysis of EEG recordings and neuroimaging scans such as MRI and PET enables neurologists to detect subtle structural and functional brain changes that may otherwise remain unnoticed. These capabilities are particularly valuable in epilepsy, stroke, neuro‑oncology, and neurodegenerative disorders, where early detection and precise mapping of disease progression are critical. Furthermore, AI facilitates predictive analysis to anticipate complications, personalise interventions, and optimise treatment strategies, thereby improving patient outcomes. Despite these advancements, challenges persist, including limited transparency in algorithmic outputs, lack of standardised frameworks, data bias arising from restricted training sets, and ethical concerns regarding privacy and equitable access. Addressing these limitations through stronger policies, robust validation studies, and equitable deployment will be essential to build clinician confidence and ensure safe integration of AI into routine practice. Ultimately, AI is emerging as a valuable addition to clinical expertise, with its ability to integrate neuroimaging, genomics, and clinical data for personalised neurology.