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Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored.

Recent advances in large language models (LLMs), especially reasoning LLMs have demonstrated impressive reasoning capabilities in specialized domains. The purpose of this study is to evaluate the performance of new open-source reasoning LLM, DeepSeek-R1 and other contemporary LLMs on radiology board examination questions, comparing their accuracy to human radiologists.

This study aims to evaluate various large language models (LLMs) for their effectiveness in answering Japan Radiology Board Examination (JRBE).

Background/Objectives

Multimodal large language models (LLMs) have shown potential in processing both text and image data for clinical applications. This study evaluated their diagnostic performance in identifying retinal diseases from optical coherence tomography (OCT) images.

This study aimed to evaluate the feasibility of general-purpose large language models (LLMs) in addressing inequities in medical licensure exam preparation for Thailand’s National Medical Licensing Examination (ThaiNLE), which currently lacks standardized public study materials.

This study aimed to explore the capabilities of advanced large language models (LLMs), including OpenAI's GPT-4 variants, Google's Gemini series, and Anthropic's Claude series, in addressing highly specialized otolaryngology board examination questions. Additionally, the study included a longitudinal assessment of GPT-3.5 Turbo, which was evaluated using the same set of questions one year ago to identify changes in its performance over time.

Azospirillum sp. is a plant growth-promoting rhizobacteria largely recognized for its potential to increase the yield of different important crops. In this work, we present a thorough genomic and phenotypic analysis of A. argentinense Az39

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways is useful but cannot keep up with the exponential growth of the literature. Large-scale language models (LLMs), notable for their vast parameter sizes and comprehensive training on extensive text corpora, have great potential in automated text mining of biological pathways.