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Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study

(A) Accuracy, (B) completeness, (C) humanity, (D) readability, (E) safety, and (F) radar chart. Descriptive statistics for the objective readability analysis are shown in Tables 4 and 6 and Figure 3. Specifically, Figures 3 A-3 C display the variations in character count, reading difficulty, and recommended reading age across the 4 LLMs. Figure 3 D shows the distribution of reading difficulty scores, and Figure 3 E presents the proportions of education levels required to understand the responses.

Shiqi Qiang, Haitao Zhang, Yang Liao, Yue Zhang, Yanfen Gu, Yiyan Wang, Zehui Xu, Hui Shi, Nuo Han, Haiping Yu

J Med Internet Res 2025;27:e73226

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The Effect of Overcoming the Digital Divide on Middle Frontal Gyrus Atrophy in Aging Adults: Large-Scale Retrospective Magnetic Resonance Imaging Cohort Study

Executive function was tested using the Stroop Color Word Test and the Trail Making Test Part B [30]. Spatial processing was assessed using the Clock Drawing Test [31] and the RO_Copy test [29]. Attention was evaluated using the Symbol Digit Modification Test [32] and the Trail Making Test Part A [30]. Language was tested using the Boston Naming Test and the Verbal Fluency Test (VFT) [33].

Yumeng Li, Xinyue Zhang, Jiaqing Sun, Junying Zhang, Aiqin Zhu, Xin Li, Zhanjun Zhang

J Med Internet Res 2025;27:e73360

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Associations Between Daily Symptoms and Pain Flares in Rheumatoid Arthritis: Case-Crossover mHealth Study

(B) Two scenarios of exclusion. Left: the onset (day 4) meets the definition, but the pain severity is at the personal median pain level, conflicting with the criterion for the end of a pain flare. Right: 2 flare onsets (days 2 and 4) meet the definition but end on the same day (day 5). The second onset (day 4) is removed to avoid double counting. In addition to identifying pain flares by their severity, we examined their residual impact in the postflare phase.

Ting-Chen Chloe Hsu, Belay B Yimer, Pauline Whelan, Christopher J Armitage, Katie Druce, John McBeth

JMIR Mhealth Uhealth 2025;13:e64889

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Symptom Trajectories and Clinical Subtypes in Post–COVID-19 Condition: Systematic Review and Clustering Analysis

(A) 3rd-month follow-up, (B) 6th-month follow-up, (C) 12th-month follow-up, and (D) 24th-month follow-up; nodes represent individual symptoms and edges to illustrate the correlations between symptoms for each interval. Each node’s color and size represent the degree of the symptom, with darker colors and larger size indicating a higher number of symptoms correlated with it.

Mingzhi Hu, Tian Song, Zhaoyuan Gong, Qianzi Che, Jing Guo, Lin Chen, Haili Zhang, Huizhen Li, Ning Liang, Guozhen Zhao, Yanping Wang, Nannan Shi, Bin Liu

JMIR Public Health Surveill 2025;11:e72221

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

The demographic and image attributes of participants within our study’s datasets are detailed in Table S1 in Multimedia Appendix 1, with the distributions of ocular alignment angle (Training set: median 0, range −12.603-5.737; Internal Testing set: median 0, range −5.713-5.737) and eye region bounding box area (Training set: median 0.134, range 0.022-0.305; Internal Testing set: median 0.127, range 0.024-0.249) depicted in Figure S2 A,B in Multimedia Appendix 1.

Dawen Wu, Yanfei Li, Zeyi Yang, Teng Yin, Xiaohang Chen, Jingyu Liu, Wenyi Shang, Bin Xie, Guoyuan Yang, Haixian Zhang, Longqian Liu

J Med Internet Res 2025;27:e74402

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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

(B) Confusion matrix analysis of the 8 models in the internal validation cohort. ACC: accuracy; AUC: area under the curve; GBM: gradient boosting machine; LASSO: least absolute shrinkage and selection operator; LGB: light gradient boosting machine; LR: logistic regression; MCC: Matthews correlation coefficient; RF: random forest; RIDGE: ridge regression; SEN: sensitivity; SPE: specificity; SVM: support vector machine; XGB: extreme gradient boosting.

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