Journal of Medical Internet Research

Internet-based interventions can improve treatment accessibility, prevent chronicity, and reduce waiting times. Despite their potential, the effectiveness of transdiagnostic internet-based interventions for individuals on waiting lists, and their integration into routine care, remains insufficiently evaluated.

Digital health literacy (DHL), the ability to seek, understand, and apply digital health information, is increasingly important in the United Kingdom, with a focus on digital transformation within the health service. While digital tools offer potential to improve access and equity, they may exacerbate existing health inequities if segments of the population are unable to engage with them effectively. Understanding the sociodemographic, economic, and social factors associated with DHL is essential to designing inclusive digital health services.

Large language models (LLMs) are increasingly being explored for clinical decision support. However, whether inference-only LLM outputs can be interpreted as reliable quantitative risk estimates in structured clinical prediction remains unclear.

Obstructive sleep apnea (OSA) affects 38% of the population, yet over 90% of cases remain undiagnosed. The gold standard for diagnosis, polysomnography, requires specialized equipment and trained personnel, making it inaccessible in primary care and acute settings. With artificial intelligence (AI) advancements, oximetry-based AI models have emerged as potential alternatives for OSA diagnosis.

Clinical documentation burden contributes significantly to physician burnout, with health care professionals spending much of their time on electronic health record interactions. Automatic speech recognition (ASR) systems offer a promising solution; however, their application in Korean medical settings faces unique challenges due to widespread Korean-English code-switching, where clinicians routinely alternate between Korean conversational language and English medical terminology within single utterances.

Mental disorders affect nearly one-third of adults in Germany, with a 12-month prevalence of approximately 28%. Following Germany’s 2019 Digital Care Act, digital health applications (Digitale Gesundheitsanwendungen [DiGA]) became reimbursable interventions by the statutory health insurance for mental health conditions. However, adoption remains uneven. General practitioners (GPs) issue most mental health DiGA prescriptions, while psychotherapists or psychiatrists prescribe far fewer, even though most DiGA target mental health. Existing studies imply profession-specific barriers but lack quantitative evidence on preference drivers or remain descriptive. Whether and how these preferences differ across professional groups has not been systematically quantified.

Sexually transmitted infections (STIs) are at a record high in the United States and are a significant health problem for childbearing women. Rates of substance use, particularly cannabis and opioid use, have increased in recent years and are linked to negative health consequences for pregnant women and their infants. Addressing these health concerns together during this vulnerable time is a priority.

Causality assessment is central to pharmacovigilance but remains resource-intensive and subjective. The applicability of large language models (LLMs) to formal World Health Organization–Uppsala Monitoring Centre (WHO-UMC) drug–adverse event causality assessment has not been well established.

Adolescents and young adults (AYAs) with complex congenital heart disease (CHD) may benefit from eHealth interventions, particularly when early signs of deterioration or self-management challenges arise. eHealth can enhance self-management skills and communication with health care professionals (HCPs). However, systematic reviews examining eHealth interventions for this population remain limited and heterogeneous, indicating a need for a systematic review of the literature to guide the development, implementation, and evaluation of such interventions.

The global COVID-19 vaccine rollout faces challenges from persistent hesitancy, especially in rural and underserved regions. Alaska’s unique geographic, cultural, and infrastructural challenges create complex dynamics for vaccine uptake.

Limited data availability and privacy constraints hinder the development of robust survival prediction models for personalized treatment. Synthetic data offers a promising solution, preserving the statistical properties of real clinical data.