The Intersection Between Neuropathology and Artificial Intelligence: A Review

Artificial intelligence (AI) has become increasingly relevant in computational pathology, with expanding applications in neuropathology and neuro-oncology. To provide an overview of the main applications of AI in the evaluation of central nervous system tumors, focusing on diagnostic, prognostic, and integrative approaches, a structured literature search was performed in PubMed, Web of Science, and Scopus, including studies published between 2006 and 2025 that addressed the use of AI in neuropathology and neuro-oncology. AI models have been applied to multiple tasks, including intraoperative diagnosis, tumor classification, prediction of molecular alterations, and integration of multi-omics data. Deep learning approaches, particularly convolutional neural networks and multimodal models, demonstrated high accuracy in several studies. In addition, emerging approaches such as foundation models and large language models have further expanded the scope of AI applications in neuropathology. In conclusion, AI shows significant potential to improve diagnostic accuracy, prognostic assessment, and personalized treatment in neuro-oncology. However, challenges such as data heterogeneity, lack of external validation, and barriers to clinical implementation remain.