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Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis

(C) The forest map of the top 5 predictors using multivariate logistic regression in small-sized follicular tumors, and the OR and 95% CI of microcalcification, macrocalcification, peripheral calcification, and microcalcification with comet-tail artifacts is based on no computed echogenic foci.

Xin Li, Wen-yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-ze Hu, Yang Yang, Yi-hang Yang, Jing-yao Liu, Chun-Hui Yuan, Zheng Liu

JMIR Cancer 2025;11:e73069

Regulatory Insights From 27 Years of Artificial Intelligence/Machine Learning–Enabled Medical Device Recalls in the United States: Implications for Future Governance

Regulatory Insights From 27 Years of Artificial Intelligence/Machine Learning–Enabled Medical Device Recalls in the United States: Implications for Future Governance

The Guidance of Initiation of Voluntary Recalls Under 21 CFR Part 7, Subpart C mentions referencing 21 CFR 820.100(a)(2), which requires procedures for implementing corrections or preventive actions to include an investigation of the causes for nonconformities related to the product, processes, and quality systems.

Wei-Pin Chen, Wei-Guang Teng, C Benson Kuo, Yu-Jui Yen, Jian-Yu Lian, Matthew Sing, Peng-Ting Chen

JMIR Med Inform 2025;13:e67552

Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline

Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline

Li et al [81] drew development and deployment road maps of artificial general intelligence (AGI; see the glossary in Multimedia Appendix 1) models (mainly MLLMs) in medical imaging while also providing key insights into potential challenges and pitfalls. While there is no consensus on what AGI is, one may view an AGI system as a form of artificial intelligence (AI) with a general scope with the ability to perform well across various goals and contexts [17].

HongYi Li, Jun-Fen Fu, Andre Python

J Med Internet Res 2025;27:e71916

Correlation of Biomechanical Variables of Lower Extremity Movement During Functional Tests and Tasks in Youth League Football Players: Cross-Sectional Correlation Study

Correlation of Biomechanical Variables of Lower Extremity Movement During Functional Tests and Tasks in Youth League Football Players: Cross-Sectional Correlation Study

All correlations are statistically significant (P a(°): degrees - Joint angles. b VL: vastus lateralis. c(m V): millivolts. d BF: biceps femoris. e(a.u.): arbitrary units. f GMx: gluteus maximus. g GM: gluteus medius. Moderate negative correlations were also identified in the study. Hip joint internal rotation was negatively correlated with COP2 W (ρ=-0.368, P The study also examined correlations based on gender distribution (see Table 3).

Anna Davidoviča, Sergejs Davidovičs, Guna Semjonova, Alexei Katashev, Alexander Oks, Linda Lancere, Signe Tomsone, Maksims Zolovs

JMIR Form Res 2025;9:e69046

Developing a Framework for Online Review-Based Health Care Service Quality Assessment: Text-Mining Study

Developing a Framework for Online Review-Based Health Care Service Quality Assessment: Text-Mining Study

Cue distribution among reviews (A and C) and doctors (B and D). We provide descriptive statistics for the variables in the econometric model in Table S4 in Multimedia Appendix 1. In terms of the 5 dimensions of doctor service quality, we derived—expertise, service delivery process, communication attitude, empathy, and outcome—patients perceive higher quality in doctors’ communication attitude and expertise, with scores of 0.06 and 0.04, respectively.

Xue Zhang, Jianshan Sun, Xin Li, Yezheng Liu, Chenwei Li

J Med Internet Res 2025;27:e66141