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Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review

Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review

The MLOps workflow pipeline from all 19 studies discussed the implementation of a data extraction step, where the data used for developing the ML model were extracted from a pre-existing database [35,41-45,47,48,50,52,53], or the collection of data from various electronic health records or the collection of patient physiological measurements from clinical settings [36-40,46,49]. Markowitz et al [51] obtained data from a combination of pre-existing databases and in-hospital measurements.

Yutong Li, Julie Tian, Ariana Xu, Russell Greiner, Jake Hayward, Andrew James Greenshaw, Bo Cao

J Med Internet Res 2025;27:e66559


The Elastic Electronic Health Record: A Five-Tiered Framework for Applying Artificial Intelligence to Electronic Health Record Maintenance, Configuration, and Use

The Elastic Electronic Health Record: A Five-Tiered Framework for Applying Artificial Intelligence to Electronic Health Record Maintenance, Configuration, and Use

Tier I consists of autonomous database reconfiguration, operating similarly to the Oracle Autonomous Database with automated tuning, patching, and workload balancing [1]. This tier creates a change log for retroactive review, with examples including component upgrades, system maintenance suggestions, software error detection, cyber security threat detection, and supplemental database backups.

Colby Uptegraft, Kameron Collin Black, Jonathan Gale, Andrew Marshall, Shuhan He

JMIR AI 2025;4:e66741


Telehealth Interventions in Pharmacy Practice: Systematic Review of Reviews and Recommendations

Telehealth Interventions in Pharmacy Practice: Systematic Review of Reviews and Recommendations

The database search returned 579 titles across the 4 databases, including duplicates. The results from each database were exported to End Note to remove duplicates, and a manual search was conducted to ensure all duplicates were removed before proceeding to the screening process. Removal of duplicates resulted in a total of 386 reviews to be included in the screening, with 18 articles being included in this review. The PRISMA Checklist can also be found as Multimedia Appendix 2.

Rachel Lai Kay Chong, Andrew Siang Ee Chan, Crystal Min Siu Chua, Yi Feng Lai

J Med Internet Res 2025;27:e57129


Paving the Road for More Ethical and Equitable Policies and Practices in Telerehabilitation in Psychology and Neuropsychology: Protocol for a Rapid Review

Paving the Road for More Ethical and Equitable Policies and Practices in Telerehabilitation in Psychology and Neuropsychology: Protocol for a Rapid Review

Initial electronic database searches have retrieved 566 reviews and 2751 original articles meeting the search criteria. Articles have been exported to Covidence and were screened in accordance with the inclusion and exclusion criteria by 2 independent reviewers. Ultimately, 17 reviews and 82 original articles meeting the eligibility criteria were included for data extraction. Preliminary results from data extraction identified key concern dimensions with a focus on ethical and equitable aspects of TR.

Dorothée Morand-Grondin, Jeanne Berthod, Jennifer Sigouin, Simon Beaulieu-Bonneau, Dahlia Kairy

JMIR Res Protoc 2025;14:e66639


Making Medical Education Courses Visible: Theory-Based Development of a National Database

Making Medical Education Courses Visible: Theory-Based Development of a National Database

The first step was establishing a comprehensive database of existing educational offerings. This database facilitates an iterative and reciprocal examination of the course landscape, identifying gaps in the current educational landscape and recognizing requirements that may diverge from international frameworks. This approach ensures that the subsequent framework development and the certification of educators and courses are tailored to the unique needs and priorities of the Swiss medical education system.

Andi Gashi, Monika Brodmann Maeder, Eva K. Hennel

JMIR Med Educ 2025;11:e62838


Media Framing and Portrayals of Ransomware Impacts on Informatics, Employees, and Patients: Systematic Media Literature Review

Media Framing and Portrayals of Ransomware Impacts on Informatics, Employees, and Patients: Systematic Media Literature Review

Some subscriptions of Newspaper Source may include the Associated Press Newswire database. The Associated Press Newswire is a full-text database that contains harvested news from the Associated Press, and when the user clicks a link to a news story, they are sent to the original article [40]. We used the following search string to ensure an exhaustive and broad but relevant return of records: “(hospital OR healthcare OR clinic OR medical) AND (ransomware OR denial of service OR cybersecurity).”

Atiya Avery, Elizabeth White Baker, Brittany Wright, Ishmael Avery, Dream Gomez

J Med Internet Res 2025;27:e59231


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