neuroGPT-X: Towards an Accountable Expert Opinion Tool for Vestibular Schwannoma
Authors: Guo Edward , Gupta Mehul , Sinha Sarthak , Rössler Karl , Tatagiba Marcos , Akagami Ryojo , Al-Mefty Ossama , Sugiyama Taku , Stieg Philip E. , Pickett Gwynedd E. , de Lotbiniere-Bassett Madeleine , Singh Rahul , Lama Sanju , Sutherland Garnette R. 
Published: February 2023

Background

The global launch of ChatGPT on November 30, 2022 has sparked widespread public interest in large language models (LLMs), and interest in the medical community is growing. Indeed, recent preprints on medRxiv have examined ChatGPT and GPT-3 in the context of standardized exams, such as the United States Medical Licensing Examination. These studies demonstrate modest performance relative to national averages. In this work, we enhance OpenAI’s GPT-3 model through zero-shot learning, anticipating that it outperforms experienced neurosurgeons in written question-answer tasks for common clinical and surgical questions on vestibular schwannoma. We aimed to address LLM accountability by including in-text citations and references to the responses provided by GPT-3.

Methods

The analysis involved (i) creating a dataset through web scraping, (ii) developing a chat-based platform called neuroGPT-X, (iii) enlisting expert neurosurgeons across international centers to create and answer questions and evaluate responses, and (iv) analyzing the evaluation results on the management of vestibular schwannoma. The survey had a blinded and unblinded phase. In the blinded phase, a neurosurgeon with 30+ years of experience curated 15 questions regarding common clinical and surgical contexts of vestibular schwannoma. Then, four neurosurgeons, ChatGPT (January 30, 2023 model, akanaiveGPT), and a context-enriched GPT model independently provided their responses. Three experienced neurosurgeons blindly evaluated the responses for accuracy, coherence, relevance, thoroughness, speed, and overall rating. Then, all seven neurosurgeons were unblinded to all responses and provided their thoughts on the potential of expert LLMs in the clinical setting.

Findings

Both the naive and content-enriched GPT models provided faster responses to the standardized question set (p<0.01) than expert neurosurgeon respondents. Moreover, responses from both models were consistently non-inferior in accuracy, coherence, relevance, thoroughness, and overall performance, and were often rated higher than expert responses. Importantly, context enrichment of GPT with relevant scientific literature did not significantly affect speed (p>0.999) or performance across the aforementioned domains (p>0.999). 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. Further, we developed neuroGPT-X, a chat-based platform designed to provide point-of-care clinical support and mitigate 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.

Interpretation

A context-enriched GPT model provided non-inferior responses compared to experienced neurosurgeons in generating written responses to a complex neurosurgical problem for which evidence-based consensus for management is lacking. We show that context enrichment of LLMs is well-suited to transform clinical practice by providing subspecialty-level answers to clinical questions in an accountable manner.

Research in Context

Evidence before this study

We searched PubMed for “(vestibular schwannoma OR acoustic schwannoma) AND (GPT-3 OR Generative Pretrained Transformer OR large language model)” with no filters and identified no relevant articles. We then searched PubMed using the string “(subspecialty OR neurosurgery OR physician) AND (GPT-3 OR Generative Pretrained Transformer OR large language model) AND (fine-tuning OR context enrichment)” with no filters and identified three studies. One study noted that domain-specific knowledge enhanced pre-trained language models.

Added value of this study

To our knowledge, this is the first study to show the non-inferiority of a context-enriched LLM in a question-answer task on common clinical and surgical questions compared to experienced neurosurgeons worldwide, determined by their neurosurgical colleagues. Furthermore, we developed the first online platform incorporating an LLM, chat memory, in-text citations, and references regarding comprehensive vestibular schwannoma management. To assess the model’s performance, a neurosurgeon with 30+ years of experience managing patients with vestibular schwannoma curated 15 questions to the model, ChatGPT, and four international expert neurosurgeons. A separate, blinded group of three expert neurosurgeons assessed these answers for accuracy, coherence, relevance, thoroughness, speed, and overall rating. This study demonstrated the capability of context-enriched LLMs as point-of-care informational aids. Importantly, all expert surgeons raised questions regarding the nuances and role of human experience and intuition that GPT may not capture in generating opinions or recommendations.

Implications of all the available evidence

The present study, with its subspecialist-level performance and interpretable results, suggests that context-enriched LLMs show promise as a point-of-care medical resource. Evaluations from experienced neurosurgeons showed that a context-enriched GPT model was rated similarly to neurosurgeon responses across evaluation domains in this study. This work serves as a springboard for expanding this tool into more medical specialties, incorporating evidence-based clinical information, and developing expert-level dialogue surrounding LLMs in healthcare.