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Background

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.

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.

Breast MRI, particularly dynamic contrast-enhanced (DCE) MRI, offers high sensitivity in detecting breast lesions but suffers from variability in interpretation. Artificial intelligence (AI) tools like Gemini 2.0 (Google AI, Mountain View, CA) may help streamline and improve diagnostic accuracy. This study evaluates Gemini 2.0's performance in classifying breast lesion status using application programming interface (API)-based image analysis.

Large language models (LLMs) hold transformative potential for clinical decision-making in the rapidly advancing field of AI in medicine. This study evaluates how Chain-of-Thought (CoT) prompting techniques affect medical reasoning performance with consideration for clinical applicability.

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.

A gemini surfactant imidazoline type, namely N-(3-(2-fatty-4,5-dihydro-1H-imidazol-1-yl) propyl) fatty amide, has been obtained from the fatty acids contained in the mango seed and used as a CO

This study set out to investigate the biological activity of monomeric surfactants dodecyltrimethylammonium bromide (DTAB) and the next generation gemini surfactant hexamethylene-1,6-bis-(