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Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent condition linked to type 2 diabetes and other metabolic risk factors. Timely detection of advanced fibrosis (≥F3) in MASLD patients is critical for effective clinical management. Traditional risk scores, such as the Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS), have limitations, prompting the exploration of machine learning models for improved risk prediction.

This study aims to evaluate the effect of input format and hyperparameter settings on GPT-5 and explore the contribution of GPT-5 assistance to radiologists' performance in abdominal cases.

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.

The proliferation of tobacco-related misinformation poses significant public health risks, requiring scalable solutions for credibility assessment. Traditional manual fact-checking approaches are resource-intensive and cannot match the pace of misinformation spread.

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.

Artificial intelligence (AI) can potentially assist in triaging suspicious skin lesions as malignant or benign. General-purpose multimodal large language models (LLMs), such as GPT-4o, have not been rigorously evaluated for this task. This study assessed GPT-4o's ability to triage skin lesions and compared its performance to specialised neural networks.

Background

Generative artificial intelligence, particularly large language models (LLMs), is increasingly used to navigate information, potentially shaping users' perceptions of different social groups. This study examines age-related stereotypes in LLM-generated text using natural language processing (NLP) techniques.

Mock examinations are widely used in health professional education to assess learning and prepare candidates for national licensure. However, instructor-written multiple-choice items can vary in difficulty, coverage, and clarity. Recently, large language models (LLMs) have achieved high accuracy in medical examinations, highlighting their potential for assisting item-bank development; however, their educational quality remains insufficiently characterized.

Despite increased awareness of diversity and inclusion in neurointerventional surgery, the representation of women in neurointerventional academic publishing has not been systematically quantified. We aimed to evaluate global and temporal gender trends among authors publishing in leading neurointerventional journals using natural language processing (NLP) tools.