Background The global launch of ChatGPT on November 30, 2022 has sparked widespread public interest in large language models, and interest from the medical community is growing. Indeed, recent preprints on medRxiv have examined ChatGPT and GPT-3 in the context of standardized exams, including the United States Medical Licensing Examination and the Ophthalmic Knowledge Assessment Program. These studies demonstrate modest performance relative to national averages. In this work, we enhanced OpenAI's GPT-3 model through zero-shot learning, anticipating it outperforms experienced neurosurgeons. Furthermore, we aimed to address accountability by including in-text citations and references to the responses provided by GPT-3. Methods The analysis comprised the (i) development of a structured dataset via web scraping, (ii) development of a chat-based platform called neuroGPT-X, (iii) recruitment of experienced neurosurgeons across international centers to create questions and evaluate GPT and surgeon responses regarding the comprehensive management of vestibular schwannoma, and (iv) analysis of evaluation results. The survey was split into blinded and unblinded phases. In the blinded phase, a neurosurgeon with 30+ years of experience managing patients with vestibular schwannoma curated 15 questions regarding common clinical and surgical contexts of vestibular schwannoma. Then, four neurosurgeons, ChatGPT (January 30, 2023 model, aka naive GPT), and a context-enriched GPT model independently provided their responses. Three experienced neurosurgeons blindly evaluated the responses across accuracy, coherence, relevance, thoroughness, speed, and overall rating. All seven neurosurgeons were unblinded to all responses and provided their thoughts on the potential of expert large language models in the clinical setting. Findings Both the naive and content-enriched GPT models provide faster responses to the standardized question set (p<0.01) than expert neurosurgeon respondents. Moreover, the generated responses for both models were consistently non-inferior in accuracy, coherence, relevance, thoroughness, and overall performance, and often demonstrated superiority when compared to expert responses. Importantly, we enriched the performance of GPT with relevant scientific literature without significantly affecting speed (p>0.999) or performance across all domains (p>0.999). Further, we develop neuroGPT-X, a chat-based platform designed to improve clinical decision-making 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 was non-inferior and often outperformed experienced neurosurgeons in generating specialist-level responses to a complex neurosurgical problem. We show that the context-enrichment of large language models is well-suited to transform clinical practice by providing subspecialty-level answers to clinical questions in an accountable manner.
Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma. The prognosis for patients with ccRCC has improved over recent years with the use of combination therapies with an anti-programmed death-1 (PD-1) backbone. This has enhanced the quality of life and life expectancy of patients with this disease. Unfortunately, not all patients benefit; eventually, most patients will develop resistance to therapy and progress. Recent molecular, biochemical, and immunological research has extensively researched anti-angiogenic and immune-based treatment resistance mechanisms. This analysis offers an overview of the principles underpinning the resistance pathways related to immune checkpoint inhibitors (ICIs). Additionally, novel approaches to overcome resistance that may be considered for the trial context are discussed.
The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.
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