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http://dx.doi.org/10.1007/s00134-023-07096-7 | DOI Listing |
Nurse Educ Pract
September 2025
Department of orthopedics, the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Aim: This study aims to evaluate the application effect of Chat Generative Pre-trained Transformer (ChatGPT)-driven blended teaching model in nursing rounds.
Background: Traditional teacher-centered nursing rounds often lead to passive learning and low efficiency. It remains uncertain whether ChatGPT-based nursing rounds is superior to traditional teaching in nursing rounds.
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 PDFEur Spine J
September 2025
Ministry of Health Efeler District Health Directorate, Aydın, Turkey.
Backround: Regional anesthesia techniques are increasingly being utilized as part of multimodal analgesia strategies to reduce postoperative pain and enhance recovery following lumbar spinal surgery. In this study, the effects of erector spinae plane (ESP) block and retrolaminar block (RLB) on postoperative recovery quality and pain were compared.
Methods: Eighty patients scheduled for elective lumbar surgery were randomly assigned to either the ESP or RLB group.
Account Res
September 2025
Department of Scientific Research, The Third Xiangya Hospital, Central South University, Changsha, China.
Background: Generative Artificial Intelligence(GenAI) significantly enhances medical research efficiency but raises ethical concerns regarding research integrity. The lack of systematic guidelines for its ethical use underscores the need to investigate GenAI's impact on researchers' awareness and behavior concerning integrity.
Methods/materials: A cross-sectional survey of 718 valid responses from Chinese medical researchers assessed GenAI's impact on research integrity using an extended Unified Theory of Acceptance and Use of Technology(UTAUT) model.
JMIR Med Educ
September 2025
Department of Medical Education, Graduate School of Medicine, Chiba University, Chiba, Japan.
Background: At the beginning of their clinical clerkships (CCs), medical students face multiple challenges related to acquiring clinical and communication skills, building professional relationships, and managing psychological stress. While mentoring and structured feedback are known to provide critical support, existing systems may not offer sufficient and timely guidance owing to the faculty's limited availability. Generative artificial intelligence, particularly large language models, offers new opportunities to support medical education by providing context-sensitive responses.
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