ReviewJournal of neuro-oncology2026
Artificial intelligence for symptom assessment and patient-reported outcomes in neuro-oncology.
Review in Journal of neuro-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePatient-reported outcomes (PROs) are critical tools for measuring how patients experience their illness and treatment. Neuropsychiatric symptoms, headaches, seizures, and neurological deficits evolve over time and impact quality of life. In this narrative review, we summarize current literature on PROs in neuro-oncology, extant artificial intelligence (AI) tools for PRO analysis, considerations for real-world application, and ethical implications.
methodsWe searched PubMed/MEDLINE, Embase, and Google Scholar for English-language articles published from January 2015 through April 2026, supplemented by reference-list reviews. Four themes were agreed upon, and authors were assigned to each theme. Authors conducted a literature review, selected articles, and synthesized the information under each of these themes. An initial 78 articles were narrowed down to 53 confirming eligibility if they reported PRO or symptom data in people with brain tumors or computational/AI methods were applied to PRO or symptom monitoring in oncology. Article selection was verified by two senior authors (L.V.Y, A.S.). Older, foundational studies were retained where they remain the primary evidence.
resultsThe growing volume and complexity of PRO data present challenges for traditional analysis methods. Integrating AI, including machine learning (ML) and large language models (LLMs), may help model longitudinal symptom trajectories to predict deterioration, extract symptoms from free-text clinical notes, and embed outputs in clinical decision support tools. Autonomy, informed consent, data security, bias, access, transparency and accountability should be considered.
conclusionSuccessful translation will require interpretable, electronic health record-integrated, bias-mitigating tools that allow for actionable insights and preserve patient consent, privacy, and autonomy in this highly vulnerable population. CLINICAL TRIAL NUMBER: Not applicable.
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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.