Human-Edited Generative AI-Assisted Multiple-Choice Questions in Postgraduate Family Medicine: Blinded Cross-Sectional Comparative Psychometric Study [0.03%]
基于生成式人工智能辅助的多选题在研究生全科医学中的盲交叉横断面心理计量学研究
Kai Ping Sze,Jia Qing Lim,Yng Miin Loke et al.
Kai Ping Sze et al.
Background: Generative artificial intelligence (GenAI) is increasingly used to draft multiple-choice questions (MCQs) for health professions education, but much evidence concerns raw model outputs, expert ratings, or item...
Comparative Study
JMIR medical education. 2026 Aug 10:12:e100179. DOI:10.2196/100179 2026
Prompt Framing and Evidence Requirement for AI-Generated Educational Responses in Dental Education: Experimental Study [0.03%]
牙科教育中生成式AI回答的提示设计与证据需求：实验研究
Man Hung,Corban Ward,Owen Cohen et al.
Man Hung et al.
Background: Large language models are increasingly used in health professions education; however, the role of prompt design in shaping their outputs remains poorly understood in clinical training contexts. In dentistry, w...
Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey [0.03%]
法国卫生专业人员和学生对医疗保健领域人工智能的使用、担忧和看法：基于网络的横断面调查
Aurelia Alati,Grégoire Pigné,Carole-Anne Brugère et al.
Aurelia Alati et al.
Background: AI is increasingly discussed and deployed in health care, yet safe and effective implementation depends on the preparedness, trust, and training of the professionals who are expected to use these tools. ...
Attitudes Toward Digital Planetary Health Before and After a Lecture Series on Environmental Awareness and Future Visions: Repeated Cross-Sectional Survey Study Regarding Twin Transformation [0.03%]
环境意识和未来愿景讲习班前后的数字 planetary health 态度调查：双变革横断面调查研究
Julia Nitsche,Theresa Sophie Busse,Sonja Schmalen et al.
Julia Nitsche et al.
Background: The focus on sustainability in university teaching and education about the climate catastrophe is constantly increasing and is essential for creating change. The chances offered by digital technological innova...
Evaluating Large Language Model-Based Automated Scoring in a Voice-Based Virtual Standardized Patient Platform for Medical Students: A Cross-Sectional Agreement Study [0.03%]
基于大型语言模型的自动评分在医学学生语音虚拟标准患者平台上的评价：横断面一致性研究
Xiaoxing Gao,Xiaoming Huang,Rongrong Hu et al.
Xiaoxing Gao et al.
Background: Large language model (LLM)-powered virtual standardized patients (VSPs) enable scalable clinical skills practice, but the validity of AI-generated scores relative to faculty ratings remains unclear. ...
Correction: Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study [0.03%]
对ChatGPT在美国和中国护士执业资格考试中的表现进行校正：横断面研究
Zelin Wu,Wenyi Gan,Zhaowen Xue et al.
Zelin Wu et al.
Benefits of Chain-of-Thought Prompting for Clinical Record Rubric Evaluation in Undergraduate Medical Education: Experimental Evaluation Study With Medical Faculty [0.03%]
基于医学课程教师的实验评估研究：链式思维提示在本科生临床记录评分评估中的益处
Alberto Nogales,Sophia Denizon,Alonso Mateos Rodriguez et al.
Alberto Nogales et al.
Background: Large language models in artificial intelligence have been among the tools with a significant and real impact on people's daily lives. In this regard, they serve as an aid in specific fields, such as education...
Performance of GPT-4o and Claude in Medical Ethics Scenarios: Comparative Study [0.03%]
医学伦理场景中GPT-4o和Claude的性能比较研究
Karishma R Desai,Anna L Gorsky,Nicole A Zelenski
Karishma R Desai
Background: The emergence of AI technology has sparked curiosity regarding the capabilities of large language models (LLMs) in the field of medicine. Minimal research exists regarding the proficiency of various AI models ...
Comparative Study
JMIR medical education. 2026 Jul 23:12:e70199. DOI:10.2196/70199 2026
Correction: Augmented Reality in Surgical Training: Systematic Review of Its Impact on Technical Performance in Surgical Trainees [0.03%]
修正：外科训练中的增强现实：其对受训者技术表现影响的系统回顾
Mahmoud El Ashry,Ahmed El Ashry,Hamza Khalique et al.
Mahmoud El Ashry et al.
Principles for Responsible AI in Health Professions Education, Research, and Care: Health CARE-AI (Contextual, Accountable, Responsible, and Equitable Artificial Intelligence) Framework Delphi Consensus Study [0.03%]
医疗专业教育、研究和护理中负责任的人工智能原则：健康CARE-AI（情境化、有问责性、负责任和公平的人工智能）框架德尔菲共识研究
Lyn K Sonnenberg,David Wiljer,Muhammad Mamdani et al.
Lyn K Sonnenberg et al.
Background: Artificial intelligence (AI) is rapidly integrating into health professions education and clinical practice, creating significant opportunities alongside new ethical challenges. Although current international ...