The Role of AI-Powered Neuroimaging in Brain Tumor Diagnosis, Management, Challenges, and Future Directions

Artificial intelligence (AI) is transforming neuroimaging by enabling advanced diagnostic, prognostic, and treatment planning capabilities in brain tumor care. Through models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). AI can extract clinically meaningful features from imaging data, automate tumor segmentation, predict molecular subtypes, and support outcome forecasting. Recent developments in hybrid and multimodal frameworks that integrate imaging, clinical, and genomic information are utilised in personalized medicine in neuro-oncology. Notably, AI triage tools such as Aidoc and Viz.ai have received FDA clearance for intracranial hemorrhage detection. Deep learning models designed on the BraTS dataset are being prospectively validated for survival prediction in glioblastoma patients. Despite these advances, a major gap persists in the integration and clinical validation of multimodal AI frameworks that combine radiomics, genomics, and clinical data. Challenges such as limited generalizability, data labeling constraints, lack of interpretability, and workflow integration continue to hinder widespread adoption. Future research should prioritize federated learning, explainable AI, inclusive validation strategies, and prospective outcome-based clinical trials. With careful implementation, AI has significant potential to enhance clinical decision-making and improve outcomes for patients with brain tumors. Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 255views 83downloads Citations Download citation Regan Mujinya, Swase Dominic Terkimbi, Elna Owembabazi, et al. The Role of AI-Powered Neuroimaging in Brain Tumor Diagnosis, Management, Challenges, and Future Directions. Authorea. 27 October 2025. DOI: https://doi.org/10.22541/au.176154678.84449339/v1 DOI: https://doi.org/10.22541/au.176154678.84449339/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.