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Differences in Expert Perspectives on AI Training in Medical Education: Secondary Analysis of a Multinational Delphi Study

Qi Chwen Ong, Chin-Siang Ang, Nai Ming Lai, Rifat Atun, Josip Car

J Med Internet Res 2025;27:e72186

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Effectiveness of Mobile Health Interventions for Reducing Sitting Time in Older Adults: Systematic Review and Meta-Analysis

Siqing Chen, Chenchen Wang, Albert Ko, Carol Ewing Garber, Edward Giovannucci, Yuting Yang, Matthew Stults-Kolehmainen, Lili Yang

J Med Internet Res 2025;27:e60889

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Investigating Social Network Peer Effects on HIV Care Engagement Using a Fuzzy-Like Matching Approach: Cross-Sectional Secondary Analysis of the N2 Cohort Study

Cho-Hee Shrader, Dustin T Duncan, Redd Driver, Juan G Arroyo-Flores, Makella S Coudray, Raymond Moody, Yen-Tyng Chen, Britt Skaathun, Lindsay Young, Natascha del Vecchio, Kayo Fujimoto, Justin R Knox, Mariano Kanamori, John A Schneider

JMIR Public Health Surveill 2025;11:e64497

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Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study

Kai Du, Ao Li, Qi-Heng Zuo, Chen-Yu Zhang, Ren Guo, Ping Chen, Wei-Shuai Du, Shu-Ming Li

J Med Internet Res 2025;27:e67830

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Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

For example, Chen et al [23] developed a diagnostic model for sarcopenia based on age, cross-sectional area changes, and shear wave elasticity value changes, yielding an AUC of 0.883. Tang et al [24] developed an ultrasound-derived muscle assessment system based on muscle thickness, handgrip strength, and gait speed. The system had an overall diagnostic sensitivity of 92.7% and a specificity of 91% for sarcopenia.

Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo