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Review
. 2025 Aug 11;17(16):2625.
doi: 10.3390/cancers17162625.

AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions

Bartosz Szmyd  1   2 , Małgorzata Podstawka  1 , Karol Wiśniewski  1 , Karol Zaczkowski  1 , Tomasz Puzio  3 , Arkadiusz Tomczyk  4 , Adam Wojciechowski  4 , Dariusz J Jaskólski  1 , Ernest J Bobeff  1   5
Affiliations
Review

AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions

Bartosz Szmyd et al. Cancers (Basel). .

Abstract

Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice. Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding. Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization. Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery.

Keywords: AI; IDH; artificial intelligence; glioma; neuroradiology; neurosurgery.

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Conflict of interest statement

E.J.B. and T.P. are involved in the project entitled “RadVLLM: A Vision-Language Model for Radiology”, carried out under Action 1.1 SMART Pathway of the European Funds for a Modern Economy 2021–2027 programme.

Figures

Figure 1
Figure 1
Flowchart of article selection for the study according to PRISMA guidelines.
See this image and copyright information in PMC

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