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Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation

The dataset curated for this study was obtained from the Mayo Clinic PFT database, which houses PFT data from two distinct US regions (Midwest and Southeast), with records from February 19, 2001, to December 16, 2022. The PFTs performed on the same day—with paired spirometry and lung volume data, without the use of methacholine or a bronchodilator—were identified. Individuals under 18 years of age and patients who opted out of authorizing their data for research use were excluded from the analysis.

Scott A Helgeson, Zachary S Quicksall, Patrick W Johnson, Kaiser G Lim, Rickey E Carter, Augustine S Lee

JMIR AI 2025;4:e65456

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023)

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023)

Numerous studies using the Clinical Trials.gov database have provided further evidence since [13-19]. This enables us to compare the disclosure rates of AI/ML trials with those previously reported for non-AI/ML trials registered on Clinical Trials.gov. Finally, AI’s impact on health equity is also extensively debated.

Shoko Maru, Ryohei Kuwatsuru, Michael D Matthias, Ross J Simpson Jr

J Med Internet Res 2025;27:e60148

Associations Among Online Health Information Seeking Behavior, Online Health Information Perception, and Health Service Utilization: Cross-Sectional Study

Associations Among Online Health Information Seeking Behavior, Online Health Information Perception, and Health Service Utilization: Cross-Sectional Study

A total of 1475 cases were selected from the database for the analysis. The mean age of the respondents was 46.72 (SD 15.864) years, 794 (53.83%) were females, 1083 (73.42%) were married, and 562 (38.10%) were living in rural areas. A total of 420 respondents were western region residents, representing 28.47% (420/1475) of the total number of respondents, while 26.31% (388/1475) were central region residents, and 45.22% (667/1475) were eastern region residents.

Hongmin Li, Dongxu Li, Min Zhai, Li Lin, ZhiHeng Cao

J Med Internet Res 2025;27:e66683

Poststroke eHealth Technologies–Based Rehabilitation for Upper Limb Recovery: Systematic Review

Poststroke eHealth Technologies–Based Rehabilitation for Upper Limb Recovery: Systematic Review

The combination of key terms reported in Multimedia Appendix 1 was used for the search in each database. The searches were finalized in May 2023. Papers were excluded if they were not systematic review papers or were not written in English. According to the predefined criteria, the screening phase was based on analyzing titles and then abstracts. Later, full-paper articles of those titles or abstracts of screened publications were reviewed independently by MR and SL.

Margherita Rampioni, Sara Leonzi, Luca Antognoli, Anna Mura, Vera Stara

J Med Internet Res 2025;27:e57957

Assessment of Digital Capabilities by 9 Countries in the Alliance for Healthy Cities Using AI: Cross-Sectional Analysis

Assessment of Digital Capabilities by 9 Countries in the Alliance for Healthy Cities Using AI: Cross-Sectional Analysis

Data were collected using a database powered by a large-scale artificial intelligence (AI) application. Chat GPT (version 4.0), developed by Open AI, was accessed via an application programming interface in Python and used to conduct our survey. We installed the openai library in Python and entered an application programming interface key issued for the use of Chat GPT (version 4.0).

Hocheol Lee

JMIR Form Res 2025;9:e62935

Understanding “Alert Fatigue” in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders

Understanding “Alert Fatigue” in Primary Care: Qualitative Systematic Review of General Practitioners Attitudes and Experiences of Clinical Alerts, Prompts, and Reminders

The databases searched were the Health Technology Assessment Database, MEDLINE, MEDLINE in Process, Embase, CINAHL, Conference Proceedings Citation Index, Psyc INFO, and Open Grey, from January 1, 1960, to December 31, 2023, a date range chosen to reflect the introduction of electronic or digitally supported health care. The search was conducted by the author IG in March 2024 and no language or location limits were applied.

Illin Gani, Ian Litchfield, David Shukla, Gayathri Delanerolle, Neil Cockburn, Anna Pathmanathan

J Med Internet Res 2025;27:e62763

Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study

Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study

This was a retrospective cohort study analyzing a database provided by De SC Healthcare Inc, in which daily step count data are linked to a Japanese claims database [13,14]. Briefly, members of affiliated health insurance associations can access the Kencom smartphone app, developed by De SC Healthcare Inc, free of charge. Daily step counts were measured using the Kencom app, synchronized with smartphone pedometers [13,14].

Yoshihide Inayama, Ken Yamaguchi, Kayoko Mizuno, Sachiko Tanaka-Mizuno, Ayami Koike, Nozomi Higashiyama, Mana Taki, Koji Yamanoi, Ryusuke Murakami, Junzo Hamanishi, Satomi Yoshida, Masaki Mandai, Koji Kawakami

JMIR Cancer 2025;11:e58093