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Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFGenerative Artificial Intelligence: Applications in Scientific Writing and Data Analysis in Wound Healing Research.
Adv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFEmotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT.
BMC Nurs
January 2025
Department of Healthcare Management Research Center, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8677, Japan.
Aim: This study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover.
Methods: A single-center cross-sectional study was conducted from February 2023 to February 2024, involving semi-structured interviews with 10 operating room nurses from a national hospital in Japan. Interview transcripts were analyzed using generative AI (ChatGPT-4o) and human researchers for thematic, emotional, and subjectivity analysis.
ChatGPT Assisting Diagnosis of Neuro-Ophthalmology Diseases Based on Case Reports.
J Neuroophthalmol
October 2024
Department of Ophthalmology (YM, MD, PAL, JWF, TJH, SY), University of Tennessee Health Science Center, Memphis, Tennessee; Department of Ophthalmology (MYK), University of Colorado School of Medicine, Aurora, Colorado; and Department of Genetics, Genomics, and Informatics (SY), University of Tennessee Health Science Center, Memphis, Tennessee.
Background: To evaluate the accuracy of Chat Generative Pre-Trained Transformer (ChatGPT), a large language model (LLM), to assist in diagnosing neuro-ophthalmic diseases based on case reports.
Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases commonly seen by neuro-ophthalmic subspecialists.
A mixed-methods study comparing human-led and ChatGPT-driven qualitative analysis in medical education research.
Nagoya J Med Sci
November 2024
Center for Medical Education, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Qualitative research, used to analyse non-numerical data including interview texts, is crucial in understanding medical education processes. However, it is often complex and time-consuming, leading to an interest in technology for streamlining the analysis. This study investigated the applicability of ChatGPT, a large language model, in thematic analysis for medical qualitative research.
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