ArticleNeurosurgical review2026
Evaluating AI chatbots in neurological function test interpretation for brain tumor surgery.
Article in Neurosurgical review, 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
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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
1 citing paper in PubMed.
- Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations.Frontiers in oncology · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuropsychological assessments are essential for evaluating functional status and guiding surgical planning in patients with brain tumors. However, their complexity may hinder interpretation for patients and junior clinicians. Large language model (LLM)-based chatbots have emerged as tools providing medical information, but their ability to interpret real-world neuropsychological test results remains unevaluated. This study investigates whether LLMs can provide accurate, patient-friendly explanations of neuropsychological tests and support communication in neurosurgical care. We included 20 patients who underwent at least one of five neuropsychological tests (Seoul Neuropsychological Screening Battery, Albert Test, Line Bisection Test, Boston Naming Test, or Western Aphasia Battery) prior to brain tumor surgery. Three LLMs (ChatGPT, Copilot, and Perplexity) were prompted with standardized queries on test explanations. Responses were evaluated for readability using Flesch-Kincaid Grade Level, understandability using a modified Patient Education Materials Assessment Tool, and explanatory accuracy using an expert-rated 4-point scale. Readability scores ranged from 9.6 (Copilot) to 11.0 (Perplexity). Understandability scores were highest for ChatGPT (83.2%), followed by Perplexity (81.3%) and Copilot (66.4%). ChatGPT performed best in test purpose and methodology; Perplexity scored highest in result interpretation and average accuracy score across domains. Patient rated Perplexity highly in understandability (4.0/4.0) and usefulness (3.8/4.0). LLMs generated accurate, understandable explanations of neuropsychological tests. These tools may support multidisciplinary care and patient communication in brain tumor surgery.
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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.