人工智能与神经系统疾病的研究新进展OA

In recent years,artificial intelligence(AI)technologies have developed rapidly in the medical field and have gradually been applied to the diagnosis,treatment decision-making,and long-term management of neurological disor-ders.Neurological disorders are generally characterized by complex etiologies,marked heterogeneity in clinical manifesta-tions,diverse sources of diagnostic and therapeutic information,and a high demand for full-course disease management.Conventional diagnostic and treatment models still have certain limitations in early identification,risk prediction,prognos-tic evaluation,and individualized management.AI technologies,including machine learning,deep learning,multimodal fusion models,and large language models,can integrate medical imaging,electroencephalographic signals,clinical data,laboratory indicators,pathological and molecular testing results,omics data,and information from wearable devices.These technologies have shown potential applications in emergency stroke imaging recognition and prognostic prediction,electroencephalographic analysis and seizure monitoring for epilepsy,early screening and progression prediction for cogni-tive impairment,quantification of motor symptoms and long-term management of Parkinson disease,lesion segmentation and molecular marker prediction for brain tumors,as well as auxiliary differentiation and risk assessment of neuroimmune disorders.However,the clinical translation of AI in neurological disorders still faces several challenges,including insuffi-cient data quality and standardization,limited external validation,inadequate model generalizability and interpretability,and unclear boundaries of ethical regulation and responsibility.This review focuses on the research progress and applica-tion value of AI in major neurological disorders,and further analyzes current challenges and future development direc-tions,aiming to provide a reference for intelligent diagnosis and treatment as well as human-AI collaborative management of neurological disorders.