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

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

Improved uncertainty calibration in LLMs could enhance selective prediction methods, optimizing physician-artificial intelligence (AI) workflows in clinical settings. Future applications of a well-calibrated deprescribing CDS tool could flag cases where critical information is missing (eg, antipsychotics at unchanged doses for more than 3 months without a documented medication review).

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

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

Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial

Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial

In Hong Kong, the first AI-driven Cantonese psychological support tool, the Pai.ACT mobile app, was developed for parents of children with special education needs [11]. However, there is currently a lack of evidence on the effectiveness of AI chatbots in tackling mental health problems in the general public.

Chen Chen, Kok Tai Lam, Ka Man Yip, Hung Kwan So, Terry Yat Sang Lum, Ian Chi Kei Wong, Jason C Yam, Celine Sze Ling Chui, Patrick Ip

JMIR Hum Factors 2025;12:e65785

Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis

Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis

In this context, integrating technologies such as artificial intelligence (AI), the Internet of Things (Io T), and edge intelligence (EI) into LTC presents substantial advantages [6]. AI facilitates predictive health care by enabling timely interventions and personalized care strategies, while Io T devices such as sensors and wearables provide continuous health monitoring, enhancing resident safety and autonomy [7,8].

Shuo-Chen Chien, Chia-Ming Yen, Yu-Hung Chang, Ying-Erh Chen, Chia-Chun Liu, Yu-Ping Hsiao, Ping-Yen Yang, Hong-Ming Lin, Tsung-En Yang, Xing-Hua Lu, I-Chien Wu, Chih-Cheng Hsu, Hung-Yi Chiou, Ren-Hua Chung

J Med Internet Res 2025;27:e56692

Digital Therapeutics–Based Cardio-Oncology Rehabilitation for Lung Cancer Survivors: Randomized Controlled Trial

Digital Therapeutics–Based Cardio-Oncology Rehabilitation for Lung Cancer Survivors: Randomized Controlled Trial

With the app for HCPs, they can (1) check, modify, or confirm the artificial intelligence (AI)–driven tailored exercise prescription and send it to patients; and (2) check the feedback information from patients and optimize the exercise prescription dynamically.

Guangqi Li, Xueyan Zhou, Junyue Deng, Jiao Wang, Ping Ai, Jingyuan Zeng, Xuelei Ma, Hu Liao

JMIR Mhealth Uhealth 2025;13:e60115