Journal of Medical Internet Research

We demonstrate the feasibility of a wearable, augmented reality–based nystagmus examination system, showing its preliminary diagnostic agreement with conventional video-oculography and its potential for portable vestibular assessment in patients with vertigo.

Underserved pregnant women have a greater risk of excessive or inadequate gestational weight gain (GWG) and adverse perinatal outcomes. In the United States, the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides supplemental nutrition and is uniquely positioned to deliver equitable interventions that support recommended GWG. Yet to date, no randomized controlled trials have evaluated behavioral strategies for managing GWG in this setting.

Technology use is increasingly integrated into daily life, including among older adults, whose adoption and engagement with technology warrant closer examination. According to the matching person and technology model, technology adoption is more likely when a person’s preferences and needs align with a device’s functions and features, as well as the environment in which it is used. However, factors such as age-related changes, evolving preferences, and the rapid pace of digital transformation complicate this process. Additionally, older adults often rely on support from family members and health professionals, yet their perspectives remain largely unexplored.

Virtual healthcare models that incorporate registered nurse triage with rapid access to same-day virtual visits with clinicians represent a growing innovation in healthcare delivery. While traditional telephone advice lines focus primarily on RN-led triage and care navigation, systems such as the Department of Veterans Affairs (VA) are beginning to embed physicians and advanced practice providers directly into these platforms. This hybrid model has potential to enhance clinical responsiveness, reduce unnecessary emergency department and urgent care visits, and increase patient satisfaction by providing timely care from home.

Social determinants of health (SDOH) strongly influence clinical outcomes. Social needs are the individual-level, actionable facets of the broader SDOH framework, including food security, stable housing, and access to essential services. When these needs go unmet, they adversely affect wellbeing and quality of care. Systematically detecting social needs is therefore critical, and emerging digital tools now offer efficient, scalable approaches for screening and identification.

A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent COVID-19 cases. While diagnostic testing and connecting students and their families to appropriate resources to mitigate the spread of COVID-19 were recommended, few schools had scalable infrastructure, including information technology systems, to implement these types of measures.

The Integrated Care for Older People (ICOPE) pathway, based on an assessment of intrinsic capacity (IC), aims to achieve dynamic monitoring of the functional capacity of older adults and to provide personalized care in the community. Digital human technology, incorporating multisensory interaction, has the potential to assist older adults in self-monitoring their IC, thereby alleviating the burden of longitudinal monitoring on community health workers.

Postoperative atrial fibrillation (AF) after cardiac surgery is common and is associated with substantial clinical and economic repercussions. However, existing strategies for preventing postoperative AF remain suboptimal, limiting proactive management. Advances in artificial intelligence (AI) may improve the prediction of postoperative AF. Studies have shown that deep learning applied to electrocardiograms (ECGs) can detect subtle patterns in non-AF ECGs associated with a history of (or impending) AF (referred to as the AI-ECG-AF model). As a non-invasive test routinely performed throughout the perioperative period, the ECG presents a unique opportunity for additional risk stratification.

Background: The integration of AI into clinical workflows is advancing even before full compliance with the MyHealth@EU framework is achieved. While AI-based Clinical Decision Support Systems (CDSS) are automatically classified as high-risk under the EU AI Act, cross-border health data exchange must also satisfy MyHealth@EU interoperability requirements. This creates a dual-compliance challenge: vertical safety and ethics controls mandated by the AI Act, and horizontal semantic-transport requirements enforced through OpenNCP gateways, many of which are still maturing toward production readiness. Objective: This paper provides a practical, phase-oriented tutorial that enables developers and providers to embed AI Act safeguards before approaching MyHealth@EU interoperability tests. The goal is to show how AI-specific metadata can be included in HL7 CDA and FHIR messages without disrupting standard structures, ensuring both compliance and trustworthiness in AI-assisted clinical decisions. Regulatory foundations: We systematically analysed Regulation (EU) 2024/1689 (AI Act) and the MyHealth@EU/OpenNCP technical specifications, extracting a harmonised set of overlapping obligations. AI Act provisions on transparency, provenance, and robustness are mapped directly onto MyHealth@EU workflows, identifying the points where outgoing messages must record AI involvement, log provenance, and trigger validation. Walkthrough: To operationalise this mapping, we propose a minimal extension set, covering AI contribution status, rationale, risk classification, and Annex IV documentation links, together with a phase-based compliance checklist that aligns AI Act controls with MyHealth@EU conformance steps. Illustrative example: A simulated International Patient Summary (IPS) transmission demonstrates how CDA/FHIR extensions can annotate AI involvement, how OpenNCP processes such enriched payloads, and how clinicians in another Member State view the result with backward compatibility preserved. Discussion: We expand on security considerations (e.g., OWASP GenAI risks such as prompt injection and adversarial inputs), continuous post-market risk assessment, monitoring, and alignment with MyHealth@EU’s incident aggregation system. Limitations reflect the immaturity of current infrastructures and regulations, with real-world validation pending the rollout of key dependencies. Conclusions: AI-enabled clinical software succeeds only when AI Act safeguards and MyHealth@EU interoperability rules are engineered together from “day zero.” This tutorial provides developers with a forward-looking blueprint that reduces duplication of effort, streamlines conformance testing, and embeds compliance early. While the concept is still in its early phases of practice, it represents a necessary and worthwhile direction for ensuring that future AI-enabled clinical systems can meet both EU regulatory requirements from day one.

In low- and middle-income countries, maternal, newborn, and child health face significant challenges due to infrastructure limitations, access disparities, and service delivery inefficiencies. The Sehatmandi mobile health (mHealth) app was deployed in 2018 to address these issues across 189 health facilities in Afghanistan's Bamyan and Badakhshan provinces. This app aims to enhance service provision through real-time data monitoring, improved accountability, and performance-based health system strengthening.

Current evidence dissemination methods fall short of meeting clinical nurses’ needs, hindering the implementation of evidence-based nursing practice. Large language models (LLMs), with their advanced natural language processing capabilities, offer potential as innovative tools to facilitate evidence dissemination. However, general-purpose LLMs typically lack domain-specific knowledge, are insufficient to support effective evidence dissemination in clinical contexts. It is essential to develop artificial intelligence tools tailored to nurses’ needs and preferences to enhance evidence dissemination.

Digital health technologies, such as telehealth, remote patient monitoring, and smartphone apps, have the potential to reduce access disparities faced by rural patients with cardiovascular disease, but little is known about rural health care providers’ perspectives on adopting digital health in their practice.