Evaluating AI Chatbots in Neurological Function Test Interpretation for Brain Tumor Surgery Article Swipe
Jee‐Young Lee
,
E.–J. Lee
,
Young Il Kim
,
Seong Hoon Lim
,
Stephen Ahn
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7240530/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7240530/v1
Background 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. Methods 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 and tumor localization. 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. Tumor localization accuracy was assessed using binary scale, and a patient survey assessed the top-performing model’s perceived usefulness. Results 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 overall accuracy. Tumor localization accuracy was limited across all models (≤ 45%). Patient rated Perplexity highly in understandability (4.0/4.0) and usefulness (3.8/4.0). Conclusions 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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7240530/v1
- https://www.researchsquare.com/article/rs-7240530/latest.pdf
- OA Status
- gold
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413129796
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413129796Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-7240530/v1Digital Object Identifier
- Title
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Evaluating AI Chatbots in Neurological Function Test Interpretation for Brain Tumor SurgeryWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-08-11Full publication date if available
- Authors
-
Jee‐Young Lee, E.–J. Lee, Young Il Kim, Seong Hoon Lim, Stephen AhnList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7240530/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-7240530/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-7240530/latest.pdfDirect OA link when available
- Concepts
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Perplexity, Readability, Neuropsychology, Test (biology), Psychology, Brain tumor, Medicine, Medical physics, Artificial intelligence, Computer science, Psychiatry, Language model, Cognition, Paleontology, Programming language, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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