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Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis

Kaixin Chen, Yin Qu, Ye Han, Yan Li, Huiyan Gao, De Zheng

J Med Internet Res 2025;27:e73528

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Remote Patient Monitoring for Global Emergencies: Case Study in Patients With COVID-19

Ramin Ramezani, Wenhao Zhang, Minh Cao, Alex Bui, Antonia Petruse, Amelia Weldon, Arash Naeim

JMIR Form Res 2025;9:e66773

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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

JMIR Cancer 2025;11:e64685

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Performance of a Retinal Imaging Camera With On-Device Intelligence for Primary Care: Retrospective Study

Matthew Silvestrini, Clarissa Lui, Anil Patwardhan, Ying Chen, Tayyeba Ali, Elie Glik, Honglei Wu, Brian Levinstein, Adrianna Wenz, Nathan Shemonski, Lin Yang, Ian Atkinson, Sam Kavusi

JMIR Form Res 2025;9:e70331

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Deep Learning–Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization

Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis

Yu Chi, Huai-yu Chen, Khushboo Thaker

JMIR Form Res 2025;9:e71207

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A Web-Based Training Intervention for Primary Care Providers on Preparing Patients for Cancer Treatment Decisions and Conversations About Clinical Trials: Evaluation of a Pilot Study Using Mixed Methods and Follow-Up

Naomi D Parker, Margo Michaels, Carla L Fisher, Alyssa Crowe, Elisa S Weiss, Maria Sae-Hau, Jason Arnold, Andrea Cassells, Domenic Durante, Ji-Hyun Lee, Raymond Mailhot Vega, Ana Natale-Pereira, Taylor S Vasquez, Zhongyue Zhang, Carma L Bylund

JMIR Med Educ 2025;11:e66892

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A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation

For example, Zhang et al [46] proposed a comprehensive model using convolutional neural networks to classify 187 types of TCM diseases. Wen et al [47] also applied deep neural networks to identify tongue image data and determine patients’ constitutions, effectively addressing the imbalance problem among constitution categories.

Xiaojie Jin, Yanru Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, Jintian Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu