Journal of Medical Internet Research

Acute ischemic stroke (AIS) treatment selection requires rapid, guideline-concordant integration of clinical, imaging, and laboratory data, including therapeutic windows, contraindications, stroke severity, and imaging eligibility. This process is complex, expertise-dependent, and vulnerable to safety-critical errors.

AI-powered chatbots offer new opportunities to enhance patient education; however, their integration may reshape patterns of information interactions and trust relationships among patients, caregivers, and nurses. Evidence remains limited on how these stakeholders perceive the value and risks of AI-powered chatbots, and on their potential effects on nurse-patient trust.

As digital technologies become increasingly embedded in daily life, their roles in mental health care have expanded and diversified. Digital tools are being explored as interventions for obsessive-compulsive disorder (OCD) across the care continuum, including symptom recognition, access to care, treatment, and self-management. However, there is limited empirical understanding of how individuals living with OCD use digital technologies in situ to navigate their health care journeys or how they envision technology shaping future models of care.

Hypospadias is a common congenital malformation requiring surgery. Caregivers face substantial perioperative information needs, and large language models (LLMs) offer a potential health education channel, but their performance in pediatric urology and the relation between citation accuracy and clinical content safety lack systematic evaluation.

Understanding how sentiment toward COVID-19 mitigation measures evolves on social networks can help to inform infectious disease models and policymakers. Even though numerous studies have described social media interactions during the pandemic, few have modeled the underlying dynamics of sentiment contagion and polarization.

Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve outcomes. Online forums have become a major source of self-expression, offering new opportunities to understand mental health. However, analyzing this complex language requires new tools.

Frailty is prevalent and dynamic in older people living with HIV and is associated with adverse outcomes. Lifestyle support is recommended but difficult to deliver at scale. Digital self-access education may help, although evidence in older, multimorbid populations is limited.

Digital multidomain interventions hold promise for dementia risk reduction; however, populations at higher dementia risk, including those experiencing socioeconomic and educational disadvantage, remain underrepresented in trials, and engagement with digital interventions often declines over time. Coproduction and blended models that combine digital tools with human support may improve reach, acceptability, usability, and sustained engagement. Designing interventions that are usable and acceptable for individuals facing structural, educational, or digital barriers (underserved groups) is therefore likely to produce solutions that are both accessible and scalable for the wider midlife and older adult population.

Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development.

Multimorbidity involves heterogeneous disease combinations, treatment burden, competing priorities, and complex care pathways. Digital health interventions (DHIs) may support monitoring, self-management, and care coordination, but their effects on health-related outcomes remain uncertain.

Although thyroid nodules are detected in up to 60% of adults on ultrasound, the vast majority are benign, creating a substantial decision-making burden compounded by heterogeneous practice guidelines. Large language models (LLMs) show promise in processing unstructured medical text and are emerging as tools for report interpretation among both clinicians and patients. However, their reliability across distinct clinical tasks in thyroid ultrasound interpretation remains poorly characterized.