Artificial Intelligence in Brain Cancer: Current Applications, Challenges, and Future Prospects

Brain cancer, including primary tumors such as gliomas and glioblastomas, is one of the most complex and life-threatening neurological conditions. Diagnosis and treatment are difficult due to the brain’s sensitive structure, tumor heterogeneity, and limitations in early detection.

Artificial Intelligence (AI) is increasingly being integrated into brain cancer research and clinical workflows to improve detection, classification, and treatment planning. In medical imaging, particularly MRI scans, machine learning and deep learning models can assist in identifying tumors at earlier stages, segmenting tumor boundaries more accurately, and distinguishing between different tumor types. This improves diagnostic precision and reduces the burden on radiologists.

AI also plays a role in predicting patient outcomes, survival rates, and treatment responses by analyzing large-scale clinical and genomic datasets. This enables more personalized treatment strategies, supporting the shift toward precision medicine in neuro-oncology.

However, challenges remain, including limited high-quality annotated brain imaging datasets, variability in imaging protocols across hospitals, and concerns about model interpretability and clinical trust. Integration into real-world healthcare systems also requires strong validation and regulatory approval.

Future developments are expected to focus on explainable AI models, multi-modal data integration (MRI, genomic, and clinical data), and real-time clinical decision support systems. Ultimately, AI has the potential to significantly enhance early detection, treatment planning, and patient outcomes in brain cancer care.