ReviewFrontiers in oncology2026
Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with brain tumors often struggle to understand their condition because of complex imaging findings, multidisciplinary care pathways, and frequent cognitive and emotional vulnerability. Effective patient education is, therefore, essential but difficult to deliver within routine clinical encounters. Objective: This narrative review evaluates the role of large language models (LLMs) in supporting patient education for individuals with brain tumors. Content: We synthesize evidence from neuro-oncology, radiology, and digital health literature on the use of LLMs to explain imaging results, diagnoses, and treatment options in patient-centered language. Potential benefits include improved health literacy, accessibility, and continuity of education. Key limitations are also discussed, including hallucinations, output variability, overtrust, data privacy concerns, and ethical challenges. A clinician-guided framework for responsible integration is proposed. Conclusion: When used under clinician supervision as educational support tools, LLMs may enhance patient understanding and engagement in brain tumor care. Safe implementation will require structured governance, oversight, and alignment with ethical standards.
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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.