ReviewThe Lancet. Digital health2025
Value of artificial intelligence in neuro-oncology.
Review in The Lancet. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- ProtoSurv: prototype-guided adaptation of computed tomography foundation model for lung-cancer prognosis prediction.Visual computing for industry, biomedicine, and art · 2026Article
- Cancer Neuroscience: From Local Neuro-Immune Microenvironments to Systemic Brain-Body Circuitry.MedComm · 2026Review
- Multimodal imaging and AI in brain tumor surgery: current tools and emerging integration.npj biomedical innovations · 2026Review
- Knowledge-guided brain tumor segmentation via synchronized visual-semantic-topological prior fusion.BMC medical imaging · 2026Article
- Glioblastoma: current challenges and future horizons.Singapore medical journal · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
CNS cancers are complex, difficult-to-treat malignancies that remain insufficiently understood and mostly incurable, despite decades of research efforts. Artificial intelligence (AI) is poised to reshape neuro-oncological practice and research, driving advances in medical image analysis, neuro-molecular-genetic characterisation, biomarker discovery, therapeutic target identification, tailored management strategies, and neurorehabilitation. 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. Near-term clinical translation depends on scaling validated AI solutions for well defined clinical tasks. In contrast, more experimental 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.