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| http://dx.doi.org/10.1186/s40635-024-00678-9 | DOI Listing |
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Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFProvision of Radiology Reports Simplified With Large Language Models to Patients With Cancer: Impact on Patient Satisfaction.
JCO Clin Cancer Inform
January 2025
Department of Radiology, Dr BRAIRCH, All India Institute of Medical Sciences, New Delhi, India.
Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.
Materials And Methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.
Helping authors produce FAIR taxonomic data: evaluation of an author-driven phenotype data production prototype.
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Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON CA K1A 0C6, Canada.
It is well-known that the use of vocabulary in phenotype treatments is often inconsistent. An earlier survey of biologists who create or use phenotypic characters revealed that this lack of standardization leads to ambiguities, frustrating both the consumers and producers of phenotypic data. Such ambiguities are challenging for biologists, and more so for Artificial Intelligence, to resolve.
View Article and Find Full Text PDFAccuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study.
PLoS One
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Faculty of Dentistry, PHENIKAA University, Hanoi, Vietnam.
Objectives: This study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions.
Material And Methods: A total of 1490 MCQs from two board review books for the United States National Board Dental Examination were selected. This study evaluated six of the latest LLMs as of August 2024, including ChatGPT 4.
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