ReviewJournal of neuro-oncology2026
Opportunities and challenges of artificial intelligence and digital pathology in adult CNS tumors.
Review in Journal of neuro-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFor over a century, the pathologist's interpretation at the microscope was the final word on a brain tumor's identity. The 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, 5th edition, changed that hierarchy by introducing a layered, integrated diagnosis in which molecular findings take precedence over histology when the two conflict. Artificial intelligence (AI) applied to digitized slides adds a computational layer to this framework and marks a fundamental change in diagnostic workflow. This review examines the role of AI and digital pathology in adult CNS tumors and discusses the opportunities and challenges in clinical, neuro-oncology implementation.
methodsWe conducted a narrative review of relevant literature indexed in PubMed before and after the 2021 WHO classification, covering digital pathology, whole-slide imaging (WSI), and AI applications in adult neuro-oncology. We synthesized evidence on diagnostic performance, molecular prediction, prognostic modeling, workflow integration, and ethical considerations.
resultsAI and digital pathology show potential to improve diagnostic consistency, shorten turnaround time, and integrate histopathological, molecular, and radiological information. AI models may support glioma grading, prediction of molecular alterations from routine histology, survival stratification, prediction of brain metastasis, and intraoperative decisions. Barriers to clinical adoption include limited generalizability, lack of transparency, data privacy, and evolving regulatory oversight.
conclusionAI and digital pathology are transforming adult CNS tumor diagnostics and could improve precision, efficiency, and multidisciplinary decision-making. Successful implementation will require rigorous multi-institutional clinical validation, transparent governance, equitable access, and collaboration among patients and health care professionals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.