Journal of Medical Internet Research

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Identifying Deprescribing Opportunities With Large Language Models in Older Adults: Retrospective Cohort Study

Vimig Socrates, Donald S Wright, Thomas Huang, Soraya Fereydooni, Christine Dien, Ling Chi, Jesse Albano, Brian Patterson, Naga Sasidhar Kanaparthy, Catherine X Wright, Andrew Loza, David Chartash, Mark Iscoe, Richard Andrew Taylor

JMIR Aging 2025;8:e69504

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Sociodemographic Differences in Logins and Engagement With the Electronic Health Coach Messaging Feature of a Mobile App to Support Opioid and Stimulant Use Recovery: Results From a 1-Month Observational Study

Lindsey M Filiatreau, Hannah Szlyk, Alex T Ramsey, Erin Kasson, Xiao Li, Zhuoran Zhang, Patricia Cavazos-Rehg

JMIR Mhealth Uhealth 2025;13:e54753

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Promoting Health Literacy With Human-in-the-Loop Video Understandability Classification of YouTube Videos: Development and Evaluation Study

Xiao Liu, Anjana Susarla, Rema Padman

J Med Internet Res 2025;27:e56080

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Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey

Rui Zheng, Xiao Jiang, Li Shen, Tianrui He, Mengting Ji, Xingyi Li, Guangjun Yu

J Med Internet Res 2025;27:e62732

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Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis

In addition, both Xiao et al’s and Asthana et al’s studies [36,37] evaluated the performance of fine-tuned LLMs in nonclinical environments. Their results highlighted the significant potential of LLMs in summarizing general text through the adoption of advanced fine-tuning techniques.

Darren Liu, Xiao Hu, Canhua Xiao, Jinbing Bai, Zahra A Barandouzi, Stephanie Lee, Caitlin Webster, La-Urshalar Brock, Lindsay Lee, Delgersuren Bold, Yufen Lin