Publications by authors named "M RUSSE"
Evaluation of a context-aware chatbot using retrieval-augmented generation for answering clinical questions on medication-related osteonecrosis of the jaw.
J Craniomaxillofac Surg
January 2025
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.
View Article and Find Full Text PDFObjective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
Materials And Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2 and T2 sequences were acquired for each patient.
Acute stroke management is time-sensitive, making time data crucial for both research and quality management. However, these time data are often not reliably captured in routine clinical practice. In this proof-of-concept study we analysed image-based time data automatically captured in the DICOM format.
View Article and Find Full Text PDFArticle Synopsis
- This study explores the use of advanced deep learning methods to automatically measure body composition from whole-body MRI scans, aiming to assess their ability to predict mortality in the general population.
- The investigation was based on data from two large Western European cohort studies, focusing on key body composition metrics such as subcutaneous and visceral adipose tissue, skeletal muscle, and intramuscular fat.
- Results indicate significant associations between several volumetric body composition measures and mortality risk, highlighting the potential of automated techniques to improve clinical outcomes related to cardiometabolic diseases and cancer.
Purpose: To investigate if GPT-4 improves the accuracy, consistency, and trustworthiness of a context-aware chatbot to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing: In addition, we sought to enable auditability of the output by revealing the information source the decision relies on.
Material And Methods: We refined an existing chatbot that incorporated specialized knowledge of the ACR guidelines by upgrading GPT-3.5-Turbo to its successor GPT-4 by OpenAI, using the latest version of LlamaIndex, and improving the prompting strategy.