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Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study

Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study

This multicenter study demonstrates that the MA-YOLO–based deep learning algorithm can more accurately identify adrenal masses using contrast-enhanced abdominal CT scans, thereby improving diagnostic efficiency for clinicians with artificial intelligence (AI) assistance and holding the potential to transform current clinical practice.

Liuyang Yang, Xinzhang Zhang, Zhenhui Li, Jian Wang, Yiwen Zhang, Liyu Shan, Xin Shi, Yapeng Si, Shuailong Wang, Lin Li, Ping Wu, Ning Xu, Lizhu Liu, Junfeng Yang, Jinjun Leng, Maolin Yang, Zhuorui Zhang, Junfeng Wang, Xingxiang Dong, Guangjun Yang, Ruiying Yan, Wei Li, Zhimin Liu, Wenliang Li

J Med Internet Res 2025;27:e65937

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Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation

Reference 30: From local explanations to global understanding with explainable AI for trees chasm from model performance to clinical impact: the need to improve implementation and evaluation of AIai

Kendrick Matthew Shaw, Yu-Ping Shao, Manohar Ghanta, Valdery Moura Junior, Eyal Y Kimchi, Timothy T Houle, Oluwaseun Akeju, Michael Brandon Westover

JMIR Med Inform 2025;13:e60442

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

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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Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study

Reference 29: From local explanations to global understanding with explainable AI for trees Reference 85: 60% of Americans would be uncomfortable with provider relying on AI in their own health www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care

Jiale Dong, Zhechuan Jin, Chengxiang Li, Jian Yang, Yi Jiang, Zeqian Li, Cheng Chen, Bo Zhang, Zhaofei Ye, Yang Hu, Jianguo Ma, Ping Li, Yulin Li, Dongjin Wang, Zhili Ji

J Med Internet Res 2025;27:e68509

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