人工智能在精准医疗，尤其是神经病理学中的应用日益广泛，展现出显著的作用和潜力。基于先进的深度学习神经网络模型，人工智能不仅能够对胶质瘤进行分类分型、分级诊断，提升病理诊断的准确性和效率，而且能够预测分子改变， 节约人力、物力和财力。更为重要的是，人工智能可以进行预后预测，评估患者生存期及复发风险，为个性化的治疗和随访策 略提供参考。随着未来数据孤岛、“黑箱”算法等问题的逐步解决，人工智能有望在胶质瘤全流程诊疗中，为病理医生和临床医生的决策提供有力支持。

Artificial intelligence (AI) is increasingly utilized in precision medicine, with notable applications observed in neuropathology. In glioma diagnostics, histological classification, molecular subtyping, and WHO grading are automated by AI-based platforms, enhancing diagnostic consistency and operational efficiency. Critically, AI predicts prognosis, assesses survival and recurrence risks, and guides personalized treatment strategies. As issues like data silos and “black-box” algorithms are resolved, AI is poised to support decision-making by pathologists and clinicians throughout the clinical workflow of glioma management.