Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances

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.