The Role of AI-Powered Neuroimaging in Brain Tumor Diagnosis, Management, Challenges, and Future Directions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".