This scoping review provides recommendations for the evaluation of eHealth lifestyle interventions, specifically targeting preschool children.

This study discusses the efficacy, efficiency, and usability of a centralized portal for researchers that aggregates resources offering effective search capabilities and facilitates collaboration.

Augmented reality via head-mounted display has revolutionary potential for education, preoperative planning, and intraoperative guidance in minimally invasive orthopedic surgery, reducing K-wire placement error by half.

This study uncovered notable discrepancies between the InfoKids+ algorithmic triage and conventional nurse-based triage, highlighting the critical need for rigorous validation of such tools for accuracy and safety before public release.

The misrepresentation of hospital leadership in AI-generated images starkly contrasts with real-world trends, where women and people of color increasingly assume leadership roles.

Topological uncertainty refers to ambiguities in spatial and structural characteristics of medical features, which can impact the interpretation of coronary plaques in medical imaging.

A consensus on specific outcome measures for evaluating the impact of digital health in occupational health care is essential for obtaining easily comparable results and providing more detailed advice for health care professionals and policy makers.

DeepSeek’s approach has the potential to make artificial intelligence in health care more accessible, secure, and affordable. Health care systems must get ready for a future in which AI plays a major role in decision-making.

Evaluating the performance of AI large language models compared with obstetrics-gynecology residents in clinical decision-making, examining diagnostic accuracy and error patterns across linguistic domains, time constraints, and experience levels.

This systematic review synthesized the current state of research on natural language processing techniques for early detection of cognitive impairment from speech and language data.

Through this research, the cystic fibrosis community, academics, and clinicians developed CF Tummy Tracker, a CF-specific patient-reported outcome measure for daily recording of gastrointestinal symptoms burden.

This article is the first application of a practical guide for describing how to select a wearable device for clinical studies.

The findings suggest that participants are generally more comfortable with decentralized clinical trials than sites. Both participants and sites still prefer a hybrid clinical trial over a fully remote clinical trial operating model.

The persistent electronic alert model provides real-time risk assessment of persistent severe acute kidney injury with appropriate lead time, addressing the scarcity of validated prediction models.

Topological data analysis has emerged as a promising tool in the field of cardiovascular disease, specifically in the assessment of coronary atherosclerosis.

The integration of digital health care has improved the quality, safety, and efficiency of care, marking a shift in care delivery and empowering patients in shared decision-making.

This study implemented an inertial measurement unit sensor-based deep learning model to predict lower-limb joint kinematics during various gait activities, such as walking, jogging, and running.

While the debate around the use and regulation of large language model-based health applications progresses slowly, the number and use of these technologies is advancing rapidly. Where's the middle ground?.

Mayo Clinic Proceedings: Digital Health was launched 2 years ago with the goal of offering an international peer-reviewed medium for clinicians and scientists engaged in transformative health care to convey advances in digital medicine.

Using machine learning models to predict inpatient admissions from emergency department triage data can improve patient flow management, efficiency, and patient outcomes, and reduce overcrowding,.