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Evaluating the Effectiveness of Smart Glasses in Reducing Patient Care Time in Emergency Departments: Cohort Study From the Hangzhou Asian Games

Evaluating the Effectiveness of Smart Glasses in Reducing Patient Care Time in Emergency Departments: Cohort Study From the Hangzhou Asian Games

EDs face several common problems that hinder their efficiency, including overcrowding and resource limitations. Overcrowding results in prolonged waiting times, length of stay, diminished quality of care, and increased mortality and morbidity rates [2-5]. Furthermore, requiring diagnostic tests and specialist consultations also contribute to extended waiting times and decision-making in EDs [6-8].

Xinwei Jiang, Bangbo Xia, Mohammad Mostafa Ansari, Huiquan Jiang, Jianjiang Qi, Zhongheng Zhang, Sheng Dai, Pingping Zheng, Yang He, Ning Liu, Pengpeng Chen, Ronghua Luo, Xuchang Qin, Yansong Miao, Junru Dai, Xiaoyu Zhou, Changliang Wang, Hui Chen, Wenbin Xu, Tao Wu, Qiang Shi, Zhonghua Chen, Liping Zhou, Hao Zhang, Yun Xie, Quan Zhang, Bifa Zhou, Xiaohong Pan, Zixi Chen, Libo Zhen, Yaqing Sun, Zelin Lu, Yihao Loh, Shameera Sayer, Jennifer Mochtar, Pannika Wongpraewit, Yifan Wang, Yucai Hong

JMIR Form Res 2025;9:e65617

Large Language Model–Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Usability Study

Large Language Model–Assisted Risk-of-Bias Assessment in Randomized Controlled Trials Using the Revised Risk-of-Bias Tool: Usability Study

This research aims to explore prompt engineering methods based on the Claude (Anthropic) model to address the efficiency and consistency challenges currently faced in Ro B2 assessment practice. Our main purpose is to comprehensively evaluate whether LLMs can skillfully apply the Ro B2 tool to professionally assess RCTs and to compare its assessment results with those of human experts using strict noninferiority standards.

Jiajie Huang, Honghao Lai, Weilong Zhao, Danni Xia, Chunyang Bai, Mingyao Sun, Jianing Liu, Jiayi Liu, Bei Pan, Jinhui Tian, Long Ge

J Med Internet Res 2025;27:e70450

Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study

Clinician Attitudes and Perceptions of Point-of-Care Information Resources and Their Integration Into Electronic Health Records: Qualitative Interview Study

Although these tools have the potential to improve workplace efficiency, diagnostic accuracy, and treatment plans, few studies have examined clinician attitudes toward integrating them with EHRs [6,7]. This qualitative study examines clinician attitudes, current use, and perceived utility of POCI tools for answering medication- and disease-related questions during patient care delivery.

Marlika Marceau, Sevan Dulgarian, Jacob Cambre, Pamela M Garabedian, Mary G Amato, Diane L Seger, Lynn A Volk, Gretchen Purcell Jackson, David W Bates, Ronen Rozenblum, Ania Syrowatka

JMIR Med Inform 2025;13:e60191

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Artificial intelligence (AI) presents a solution by automating and streamlining these processes, potentially augmenting both efficiency and accuracy. However, the adoption of AI in breast cancer screening is not without challenges. Although there are over 20 Food and Drug Administration (FDA)–approved AI applications for breast imaging, their adoption and utilization in clinical settings remain highly variable and generally low [6].

Serene Goh, Rachel Sze Jen Goh, Bryan Chong, Qin Xiang Ng, Gerald Choon Huat Koh, Kee Yuan Ngiam, Mikael Hartman

J Med Internet Res 2025;27:e62941

Global Health care Professionals’ Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study

Global Health care Professionals’ Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study

Our a priori hypothesis was that while many health care professionals would recognize Chat GPT’s potential benefits, such as improving efficiency, communication, and access to knowledge, they would also express concerns regarding ethical, legal, and accuracy-related issues. This study offers timely insights for health care leaders, educators, and policymakers considering the responsible adoption of generative AI tools.

Ecem Ozkan, Aysun Tekin, Mahmut Can Ozkan, Daniel Cabrera, Alexander Niven, Yue Dong

JMIR Med Educ 2025;11:e58801

Enhancing Access to Neuraxial Ultrasound Phantoms for Medical Education of Pediatric Anesthesia Trainees: Tutorial

Enhancing Access to Neuraxial Ultrasound Phantoms for Medical Education of Pediatric Anesthesia Trainees: Tutorial

Simulation is a valuable and effective method for learners—whether used by trainees or experienced clinicians—to enhance their competency, efficiency, and confidence in performing regional anesthetic and neuraxial techniques [1-4]. Ultrasound enhances safety, decreases complications, and improves the efficacy and accuracy of neuraxial blockade in pediatric patients from preterm to adolescence [5-12].

Leah Webb, Melissa Masaracchia, Kim Strupp

JMIR Med Educ 2025;11:e63682

The Benefits of Integrating Electronic Medical Record Systems Between Primary and Specialist Care Institutions: Mixed Methods Cohort Study

The Benefits of Integrating Electronic Medical Record Systems Between Primary and Specialist Care Institutions: Mixed Methods Cohort Study

This is reflected by a specialist clinic staff who said, “Efficiency increased and productivity (of referrals) increased, because (we) don’t need to toggle between multiple applications to handle one referral.”

Kim Huat Goh, Adrian Yong Kwang Yeow, Le Wang, Hermione Poh, Hannah Jia Hui Ng, Gamaliel Tan, Soon Khai Wee, Er Luen Lim, Jared Louis Andre D’Souza

J Med Internet Res 2025;27:e49363

Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation

Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation

To focus on the most promising approaches while maintaining computational efficiency, we selected 10 datasets corresponding to the top 10 performing models from Experiment 1. This focused approach was selected to specifically examine whether the advantages observed with manual classification could be maintained when transitioning to an automated system; a key consideration for practical implementation.

Tatsuki Hasegawa, Hayato Kizaki, Keisho Ikegami, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, Satoko Hori

JMIR Med Inform 2025;13:e65371