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Real-Time, Risk-Based Clinical Trial Quality Management in China: Development of a Digital Monitoring Platform

Min Jiang, Shuhua Zhao, Yun Mei, Zhiying Fu, Yannan Yuan, Jie Ai, Yuan Sheng, Ying Gong, Jingjing Chen

JMIR Med Inform 2025;13:e64114

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Increasing the Uptake of Breast and Cervical Cancer Screening Via the MAwar Application: Stakeholder-Driven Web Application Development Study

These AI values were then summed across all relevant user needs to compute the weighted score (WS)=sum of absolute importance value, ranking the significance of each feature of the “WHATs” within the overall app structure. All these were ranked to highlight the most essential components of the MAwar application [21]. These analytical steps provided a detailed, quantified overview of the key priorities for the MAwar application’s development.

Nurfarhana Nasrudin, Shariff-Ghazali Sazlina, Ai Theng Cheong, Ping Yein Lee, Soo-Hwang Teo, Abdul Rashid Aneesa, Chin Hai Teo, Fakhrul Zaman Rokhani, Nuzul Azam Haron, Noor Harzana Harrun, Bee Kiau Ho, Salbiah Mohamed Isa

JMIR Form Res 2025;9:e65542

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Digital Therapeutics–Based Cardio-Oncology Rehabilitation for Lung Cancer Survivors: Randomized Controlled Trial

With the app for HCPs, they can (1) check, modify, or confirm the artificial intelligence (AI)–driven tailored exercise prescription and send it to patients; and (2) check the feedback information from patients and optimize the exercise prescription dynamically.

Guangqi Li, Xueyan Zhou, Junyue Deng, Jiao Wang, Ping Ai, Jingyuan Zeng, Xuelei Ma, Hu Liao

JMIR Mhealth Uhealth 2025;13:e60115

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At-Home Immersive Virtual Reality Exergames to Reduce Cardiometabolic Risk Among Office Workers: Protocol for a Randomized Controlled Trial

Jing Zhao, Akitomo Yasunaga, Andrew T Kaczynski, Hyuntae Park, Yufeng Luo, Jiuling Li, Ai Shibata, Kaori Ishii, Shohei Yano, Koichiro Oka, Mohammad Javad Koohsari

JMIR Res Protoc 2025;14:e64560

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Neurological Evidence of Diverse Self-Help Breathing Training With Virtual Reality and Biofeedback Assistance: Extensive Exploration Study of Electroencephalography Markers

Hei-Yin Hydra Ng, Changwei W Wu, Hao-Che Hsu, Chih-Mao Huang, Ai-Ling Hsu, Yi-Ping Chao, Tzyy-Ping Jung, Chun-Hsiang Chuang

JMIR Form Res 2024;8:e55478

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Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan

We delved into the hypertension landscape across Asian populations through machine learning optics, firmly anchoring our methodology within the burgeoning realm of artificial intelligence (AI)–driven disciplines. This research endeavors to amplify our comprehension of global hypertension trends by channeling multifaceted machine learning analyses, thereby catalyzing more timely and precise diagnostic efforts.

Seung Ha Hwang, Hayeon Lee, Jun Hyuk Lee, Myeongcheol Lee, Ai Koyanagi, Lee Smith, Sang Youl Rhee, Dong Keon Yon, Jinseok Lee