ReviewSurgical neurology international2026
Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.
Review in Surgical neurology international, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is rapidly advancing across medical disciplines, with neurosurgery emerging as a key field for technological innovation. In the management of brain tumors, AI-based platforms have demonstrated considerable potential to enhance diagnostic accuracy, support surgical planning, and assist intraoperative decision-making. Methods: A literature search was conducted using PubMed and Cochrane Library databases to identify peer-reviewed studies published in English between 2015 and 2025, using keywords including "artificial intelligence," "neurosurgery," "brain tumors," "machine learning," "deep learning," "computer vision," "natural language processing," "radiomics," and "surgical planning." Results: Machine learning and deep learning have improved radiologic detection, classification, and segmentation of brain tumors, while radiomics and radiogenomics enable noninvasive molecular prediction and tumor characterization. AI is increasingly integrated into surgical planning, including brain deformation modeling, fiber tractography, intraoperative histologic assessment, hyperspectral imaging, and intelligent navigation systems. Challenges include limited data availability, algorithm transparency, dataset heterogeneity, and regulatory and infrastructural requirements for clinical implementation. Conclusion: AI demonstrates considerable potential to advance brain tumor management, though robust prospective validation and evidence-based implementation are essential prerequisites for safe clinical integration. Continuous technological refinement, multidisciplinary collaboration, ongoing research, and equitable access across healthcare systems, including low-resource settings, will be essential for responsible and effective implementation.
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Identifiers
What Socratic holds
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 Socratic graph.