CNS cancers are complex, difficult-to-treat malignancies that remain insufficiently understood and mostly incurable, despite decades of research efforts.
This Review examines key opportunities and challenges associated with AI applications along the neuro-oncological care trajectory.
We highlight emerging trends in foundation models, biophysical modelling, synthetic data, and drug development and discuss regulatory, operational, and ethical hurdles across data, translation, and implementation gaps.
AI solutions offer broader potential but require technical refinement and resolution of data and regulatory challenges.
Addressing both general and neuro-oncology-specific issues is essential to unlock the full potential of AI and ensure its responsible, effective, and needs-based integration into neuro-oncological practice.
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