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

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

J Med Internet Res 2025;27:e70545

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The Applications of Large Language Models in Mental Health: Scoping Review

Yu Jin, Jiayi Liu, Pan Li, Baosen Wang, Yangxinyu Yan, Huilin Zhang, Chenhao Ni, Jing Wang, Yi Li, Yajun Bu, Yuanyuan Wang

J Med Internet Res 2025;27:e69284

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Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Jin et al [17] reported that a result from a user study using large language model framework, Trial GPT, to support patient matching resulted in a 42.6% decrease in the screening time [18]. Another AI approach is clinical trial digital twin technology [18-21]. Digital twin technology creates virtual patients that replicate individual characteristics, enabling the prediction of clinical responses [18,19,21]. By utilizing digital twins, the required sample sizes for clinical trials can be reduced [18,19,21].

Byungeun Shon, Sook Jin Seong, Eun Jung Choi, Mi-Ri Gwon, Hae Won Lee, Jaechan Park, Ho-Young Chung, Sungmoon Jeong, Young-Ran Yoon