ReviewCurrent oncology reports2025
Emerging Trends in Artificial Intelligence in Neuro-Oncology.
Review in Current oncology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThis article explores the evolving role of artificial intelligence (AI) in neuro-oncology, highlighting its potential to enhance diagnostic accuracy, predict patient outcomes, optimize treatment planning, and streamline clinical workflows. RECENT
findingsAI applications have led to significant advancements in automated tumor segmentation, molecular classification, risk stratification, treatment response evaluation, and computational pathology. AI-driven innovations have also accelerated drug discovery and leveraged natural language processing to generate structured clinical reports and extract actionable insights from unstructured data. AI has transformative potential in neuro-oncology; however, challenges like data quality, model generalizability, and clinical integration persist. Overcoming these barriers may involve new computational techniques and hardware efficiencies, as well as raising awareness, fostering interdisciplinary education, and expanding access to AI-driven tools.
Indexed as
Identifiers
40504358What 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.