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The Intersection Between Neuropathology and Artificial Intelligence: A Review
1Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo, São Paulo, Brazil.
Sage Open Pathology
|August 13, 2026
Summary
Artificial intelligence (AI) is transforming neuropathology and neuro-oncology by enhancing tumor diagnosis and prognosis. Advanced AI models show promise for personalized treatment, though clinical implementation faces hurdles.
Area of Science:
- Computational pathology
- Neuropathology
- Neuro-oncology
Background:
- Artificial intelligence (AI) is increasingly vital in computational pathology.
- Applications are expanding in neuropathology and neuro-oncology for central nervous system (CNS) tumor evaluation.
Purpose of the Study:
- To overview AI applications in CNS tumor evaluation.
- Focus on diagnostic, prognostic, and integrative approaches.
- Literature search of PubMed, Web of Science, and Scopus (2006-2025).
Main Methods:
- Structured literature search.
- Analysis of AI applications in neuropathology and neuro-oncology.
- Focus on diagnostic, prognostic, and integrative AI approaches.
Main Results:
- AI models applied to intraoperative diagnosis, tumor classification, molecular alteration prediction, and multi-omics data integration.
- Deep learning (CNNs, multimodal models) showed high accuracy.
- Foundation models and large language models are expanding AI scope.
Conclusions:
- AI holds significant potential for improving diagnostic accuracy, prognostic assessment, and personalized treatment in neuro-oncology.
- Challenges include data heterogeneity, lack of external validation, and clinical implementation barriers.
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