Rapid detection of proteins is critical in a vast array of diagnostic or monitoring applications [...].
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| http://dx.doi.org/10.3390/bios12020101 | DOI Listing |
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Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.
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Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
The potential of large language models (LLMs) in medical applications is significant, and Retrieval-augmented generation (RAG) can address the weaknesses of these models in terms of data transparency and scientific accuracy by incorporating current scientific knowledge into responses. In this study, RAG and GPT-4 by OpenAI were applied to develop GuideGPT, a context aware chatbot integrated with a knowledge database from 449 scientific publications designed to provide answers on the prevention, diagnosis, and treatment of medication-related osteonecrosis of the jaw (MRONJ). A comparison was made with a generic LLM ("PureGPT") across 30 MRONJ-related questions.
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Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan.
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The Primary School Affiliated to Yunnan University, Kunming, 650000, China.
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Department of Mathematics and Statistics, Georgia State University, Atlanta, 30303, GA, USA; Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA; Winship Cancer Institute, Emory University, Atlanta, 30322, GA, USA. Electronic address:
Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet).
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