Open Access
jzhang1961@zju.edu.cn
1508056@zju.edu.cnHE Linqian, M.S. E-mail: 22218690@zju.edu.cn
Corresponding authors, ZHANG Jing, E-mail: jzhang1961@zju.edu.cn; ZHANG Xiuming, E-mail: 1508056@zju.edu.cn
Open Access
jzhang1961@zju.edu.cn
1508056@zju.edu.cnHE Linqian, M.S. E-mail: 22218690@zju.edu.cn
Corresponding authors, ZHANG Jing, E-mail: jzhang1961@zju.edu.cn; ZHANG Xiuming, E-mail: 1508056@zju.edu.cn
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.
Key Words: glioma; artificial intelligence; neural networks; prognosis