neuroGPT-X: toward a clinic-ready large language model
Bibliographic record
Abstract
OBJECTIVE: The objective was to assess the performance of a context-enriched large language model (LLM) compared with international neurosurgical experts on questions related to the management of vestibular schwannoma. Furthermore, another objective was to develop a chat-based platform incorporating in-text citations, references, and memory to enable accurate, relevant, and reliable information in real time. METHODS: The analysis involved 1) creating a data set through web scraping, 2) developing a chat-based platform called neuroGPT-X, 3) enlisting 8 expert neurosurgeons across international centers to independently create questions (n = 1) and to answer (n = 4) and evaluate responses (n = 3) while blinded, and 4) analyzing the evaluation results on the management of vestibular schwannoma. In the blinded phase, all answers were assessed for accuracy, coherence, relevance, thoroughness, speed, and overall rating. All experts were unblinded and provided their thoughts on the utility and limitations of the tool. In the unblinded phase, all neurosurgeons provided answers to a Likert scale survey and long-answer questions regarding the clinical utility, likelihood of use, and limitations of the tool. The tool was then evaluated on the basis of a set of 103 consensus statements on vestibular schwannoma care from the 8th Quadrennial International Conference on Vestibular Schwannoma. RESULTS: Responses from the naive and context-enriched Generative Pretrained Transformer (GPT) models were consistently rated not significantly different in terms of accuracy, coherence, relevance, thoroughness, and overall performance, and they were often rated significantly higher than expert responses. Both the naive and content-enriched GPT models provided faster responses to the standardized question set than expert neurosurgeon respondents (p < 0.01). The context-enriched GPT model agreed with 98 of the 103 (95%) consensus statements. Of interest, all expert surgeons expressed concerns about the reliability of GPT in accurately addressing the nuances and controversies surrounding the management of vestibular schwannoma. Furthermore, the authors developed neuroGPT-X, a chat-based platform designed to provide point-of-care clinical support and mitigate the limitations of human memory. neuroGPT-X incorporates features such as in-text citations and references to enable accurate, relevant, and reliable information in real time. CONCLUSIONS: The present study, with its subspecialist-level performance in generating written responses to complex neurosurgical problems for which evidence-based consensus for management is lacking, suggests that context-enriched LLMs show promise as a point-of-care medical resource. The authors anticipate that this work will be a springboard for expansion into more medical specialties, incorporating evidence-based clinical information and developing expert-level dialogue surrounding LLMs in healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".