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http://dx.doi.org/10.1093/jtm/taae099 | DOI Listing |
Rev Neurol
August 2025
Servicio de Neurología, Hospital Universitario 12 de Octubre, 28041 Madrid, Español.
Introduction: The advancement of artificial intelligence (AI), particularly generative AI, has significantly transformed the field of medicine, impacting healthcare delivery, medical education, and research. While the opportunities are substantial, the implementation of AI also raises important ethical and technical challenges, including risks related to data bias, the potential erosion of clinical skills, and concerns about information privacy.
Development: AI has demonstrated great potential in optimizing both clinical and educational processes.
J Prof Nurs
September 2025
Kocaeli University of Health and Technology, Information Systems Engineering Deparment, Kocaeli, Turkey; Wefi Games Software Company, Goller Bolgesi Teknokenti, Isparta, Turkey.
Background: Comprehensive history-taking is crucial for patient assessment, prioritisation of care, and planning of care. While direct instruction methods effectively explain history-taking processes and components, they provide insufficient opportunities for practice, necessitating the implementation of supplementary teaching strategies.
Objective: This study aimed to examine the effects of AI chatbot-supported history-taking training on nursing students' questioning skills and clinical stress levels.
Nurse Educ Pract
August 2025
Department of Nursing, Mokpo National University, Muan-gun, Jeollanam-do, Republic of Korea. Electronic address:
Aim: This study aims to develop and validate an instructional debriefing model that combines question-centered learning methodology with AI prompt engineering techniques for nursing simulations.
Background: Integrating artificial intelligence (AI)-based prompt engineering into nursing simulation offers structured strategies to enhance clinical reasoning. However, current debriefing models insufficiently incorporate AI methodologies such as question-centered learning and prompt engineering, indicating a lack of theoretical and procedural frameworks METHODS: The model was developed using a four-phase approach: (1) literature review, (2) instructor interviews, (3) expert validation and (4) external evaluation of effectiveness.
Neurosurg Rev
September 2025
Department of Neurosurgery, Stanford University, Palo Alto, CA, USA.
Natural language processing (NLPs) and Large language models (LLM), such as ChatGPT, represent transformative advancements in artificial intelligence (AI). Their implementation into the medical field has a broad potential, and this review discusses the current trends and prospects of NLPs and LLMs in spine surgery, assessing their potential benefits, applications, and limitations. The methodology involved a comprehensive narrative review of existing English literature related to the use of NLPs and LLMs in spine surgery.
View Article and Find Full Text PDFBMC Health Serv Res
September 2025
Menopause Andropause Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Background: Unsafe abortion poses severe risks to women's health and well-being worldwide. In Iran, implementation of restrictive abortion rules in 2022 has intensified concerns about a potential rise in unsafe practices. This shift is likely to introduce new challenges for midwives, who are often the first point of contact in healthcare.
View Article and Find Full Text PDF