JMIR Formative Research

Symptom-based localization of brainstem ischemia is challenging because of the anatomical complexity of the brainstem and the nonspecific overlap of clinical syndromes. Whether large language models (LLMs) can meaningfully assist in this task remains uncertain.

Ecological momentary assessment (EMA) enables repeated, real-time measurement of emotional states, behaviors, and contextual exposures in individuals’ daily lives. Although EMA has been increasingly used in health and behavioral research, evidence regarding the feasibility, compliance, and acceptability of smartphone-based EMA among older adults in Asian settings remains limited.

Tuberculosis is a leading cause of death in South Africa, with poor adherence undermining treatment success. Findings from recent research on the impact of mHealth (mobile health) interventions on tuberculosis treatment outcomes show promise, yet many interventions remain untested in African contexts. Rising smartphone ownership in South Africa enables more complex mHealth interventions, offering an opportunity to deploy behavioral tools within high-burden, resource-constrained settings.

Health care workers continue to experience heightened levels of distress and burnout, which contribute to higher levels of job dissatisfaction, turnover intentions, presenteeism, and staffing shortages.

Acute kidney injury (AKI) is a frequent and serious complication among hospitalized patients, particularly in critical care settings, where its incidence can exceed 50%. AKI is associated with increased mortality, prolonged hospitalization, dialysis dependence, and higher health care costs. Although the KDIGO (Kidney Disease: Improving Global Outcomes) guidelines emphasize supportive care, hemodynamic optimization, and avoidance of nephrotoxins, their implementation remains inconsistent, partly due to the lack of timely risk stratification. Recent advances in artificial intelligence have enhanced early prediction and detection of AKI, offering new opportunities to improve patient outcomes and intensive care unit (ICU) efficiency. The U-Care Renal Platform (UCRP; U-Care Medical S.r.l), a Conformité Européenne (CE)–marked artificial intelligence–powered medical device, integrates directly with the ICU electronic health record to continuously analyze patient data and predict the risk of moderate or severe AKI within 24 hours, providing actionable, guideline-based recommendations. While the predictive performance of UCRP has been validated previously, its real-world impact on clinical and operational outcomes in the ICU remains underexplored.

Crown preparation is a technically demanding psychomotor skill in undergraduate dental education. While traditional typodont training is the gold standard, it is resource-intensive and difficult to individualize. Screen-based haptic virtual reality simulators (HVRSs) may provide a pedagogical adjunct to conventional training, but their effectiveness in supporting transfer of skills to physical tooth preparation remains unclear.

Loneliness is a prevalent concern across the United Kingdom. While validated scales exist to quantify the severity of loneliness across populations, there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma surrounding loneliness, practitioner perspectives offer crucial insights into how clients express loneliness within digital therapeutic environments. These insights can inform more nuanced conceptualizations of loneliness.

Digital remote monitoring technologies, including smartphones and wearables, offer promising avenues for early detection of psychosis relapse. However, selecting devices that are acceptable to participants and produce high-quality data remains challenging.

Asynchronous online forums provide flexible, accessible peer support for many people living with dementia and carers. Moderators are central to the functioning of these communities, yet little is known about their experiences.

Manual chart abstraction from electronic health records is a critical step in clinical outcomes research but is time-intensive and prone to human error. Advances in artificial intelligence (AI), particularly large language models, offer the potential to automate the extraction of structured data from unstructured clinical documentation with improved efficiency and consistency.

Depression in late life is often compounded by social isolation and barriers to care. There is limited study of technology-enhanced peer support for depression among older adults.

Motivational interviewing (MI) is an effective approach for supporting health behaviorchange, but face-to-face delivery is resource-intensive and difficult to scale. Rule-based conversational agents (CAs) can improve access; however, their scripted interactions and limited language flexibility constrain MI delivery. While large language models (LLMs) are increasingly being used for MI coaching, their conversational fidelity and quality compared with human coaches and rule-based CAs remain understudied.