Journal of Medical Internet Research

Suicide is a major public health challenge. Traditional assessments of suicidal thoughts and behaviors rely on retrospective measures that are subject to recall bias and show limited predictive accuracy. Ecological momentary assessment (EMA) can improve suicide risk assessment by capturing real-time information in participants’ natural environments. However, EMA burden often results in short follow-up periods.

Anticoagulants are high-alert medications with substantial risk of serious bleeding, yet dosing and prescribing errors remain common. Although clinical decision support systems (CDSSs) can mitigate these errors, their impact is constrained by alert fatigue and limited interpretability.

Traditional laboratory-confirmed influenza surveillance involves a 1- to 2-week reporting delay and captures only patients who have sought care and received a diagnosis, limiting early warning. Digital prescription data have been shown to signal influenza activity early, but their predictive value for forecasting remains unclear.

AI is increasingly embedded in digital public health systems, but model performance, system deployment, message delivery, alert volume, and user engagement do not establish whether an AI-supported workflow has changed public health practice. This viewpoint proposes a public health action end point—a prespecified, auditable, AI output–specific, actor-bound, time-bound, denominator-based way to specify and measure an existing proximal process or implementation end point. It begins with a defined AI output and records the corresponding action opportunity, accountable actor, action status, and evidence concerning the AI’s role in the action. A complete specification includes rules for repeated outputs, completed action, justified nonaction, missed action, unresolved cases, denominator loss, and paired safety, workload, privacy, equity, and model drift monitoring. A public health action end point is not a new causal outcome, reporting guideline, or validated surrogate for a population health benefit. An observed action rate alone does not establish that AI initiated or caused the action; causal claims require a documented attribution strategy and an appropriate comparator or causal design. The framework applies to research and production deployments but complements rather than replaces ethics review, trial registration, and jurisdiction-specific regulatory and governance obligations. It is intended to help authors, implementers, reviewers, and editors align claims with what an evaluation has actually measured.

This research letter summarizes the development and deployment of a data analytics dashboard that uses a simple rule-based keyword search to streamline pre–magnetic resonance imaging (MRI) safety screening for implantable medical devices, resulting in a 98% reduction in the manual screening workload while maintaining strong performance metrics.

Large language models (LLMs) are increasingly being considered for clinical decision support, yet their safety in risk-based cervical screening management remains insufficiently characterized.

Social media use disorder (SMUD) has become an important concern in adolescent digital health. Although prior research has linked problematic social media use to psychological and family-related factors, less is known about how academic stress and different patterns of family relationships are associated with SMUD among middle school students.

The COVID-19 pandemic transformed telemedicine in Poland from a niche service into a core mode of health care delivery, supported by nationwide eHealth infrastructure such as obligatory e-prescriptions and the central P1 platform. Despite this rapid expansion, concerns persist that the benefits of digital health are not shared equally, and that a digital divide limits equitable access to telemedicine services across sociodemographic and geographic groups.

AI is increasingly being integrated into diabetes care, with growing evidence supporting its potential to improve clinical decision-making, risk prediction, and self-management. However, the lived experiences, expectations, and concerns of those involved in its implementation have not been adequately synthesized.

Unobtrusive digital monitoring technologies (UDMTs) enable continuous data collection beyond episodic clinical encounters and are increasingly incorporated into digital health clinical trials. However, their successful use depends on their integration into routine clinical practice. Poor integration can increase hidden nursing workload, disrupt workflows, compromise data quality, and limit the sustainability of digital trials. Although nurses play a central role in coordinating clinical, research, and technological activities, little is known about how they engage with UDMT-enabled clinical trials in everyday practice.

Vascular compromise remains the leading cause of free-flap failure. AI-based monitoring and prediction tools have emerged as a promising adjunct for postoperative free flap monitoring and early detection of vascular compromise. Previous systematic reviews included limited evidence or broadly evaluated reconstructive outcomes.