Journal of Medical Internet Research

Digital health tools, such as health chatbots, may improve access to scalable health support, but adoption remains inconsistent. Existing models do not fully integrate technology acceptance factors with health motivation factors relevant to digital health use.

Social media has become an important channel for health information dissemination and public discussion of human papillomavirus (HPV) vaccination. Previous reviews have examined social media and HPV vaccination, often focusing on single platforms, broader HPV-related topics, or the potential effects on knowledge, leaving limited cross-platform synthesis specifically focused on HPV vaccine–related communication content, information sources, and analytical approaches.

Digital platforms are primary sources of sexual health information for young adults, yet they are widely perceived as unreliable. This tension between distrust and reliance remains undertheorized.

Quality of life (QoL) plays a crucial role in dementia care; however, QoL and its dynamic, context-dependent nature can be difficult to capture among people living with dementia due to challenges in memory and communication, and limitations of self-reported QoL instruments. Observational tools such as the Maastricht Electronic Daily Life Observation (MEDLO) provide narrative descriptions of the daily life of people living with dementia in nursing homes. However, the MEDLO tool was not developed to assess QoL specifically, and it remains unclear to what extent its narrative descriptions reflect aspects of QoL. Analyzing these narrative descriptions is labor-intensive and time-consuming. Recent advances in natural language processing, including large language models (LLMs), offer the potential to analyze these narrative descriptions at scale.

Hypertension is a leading preventable cause of cardiovascular disease, yet a substantial proportion of adults remain undiagnosed, limiting opportunities for early intervention. A predictive model was commissioned by the North West London (NWL) Integrated Care Board to identify undiagnosed hypertension. The model was developed using health records from the Whole Systems Integrated Care (WSIC) database.

AI-enabled digital health services are rapidly expanding within health care systems and are expected to improve health management and access to health information. However, rigorous empirical evidence on whether AI health service use is associated with individual health satisfaction remains limited, particularly regarding whether these potential benefits differ across socioeconomic groups.

Large language models (LLMs) are rapidly entering health care, but limited empirical data exist on health care professionals’ perceptions. Understanding health care professionals’ attitudes is essential for responsible implementation as LLMs transition from experimental to routine tools.

Clinical notes offer rich, longitudinal insights into patient health trajectories. However, existing clinical sentiment analysis primarily evaluates overall note tone rather than the patient’s distinct perspective. This gap is particularly critical in mental health care, where patient-perspective sentiment closely correlates with severe clinical outcomes, including mortality.

Health care service quality is inherently multidimensional; yet, the dominant practice in applied text analysis assigns each patient review to a single topic via Latent Dirichlet Allocation (LDA). This simplification may systematically compress evaluative information when patients discuss multiple service dimensions with varying sentiments within the same review.

Generative AI video models are increasingly capable of producing complex depictions of mental health experiences, yet little is known about how these systems represent conditions such as depression. Because AI-generated content may reach people during vulnerable periods, understanding what visual narratives these models produce for sensitive concepts carries clinical relevance.

Online health forums are used widely, yet evidence of their effectiveness is inconsistent. Evidence-based forum design guidance grounded in theory and lived experience could improve the efficacy and outcomes of these forums for the many people using them worldwide.

The widespread adoption of electronic health records (EHRs) has generated large-scale repositories of highly sensitive clinical information, emphasizing the need for robust anonymization strategies to enable secondary use for research while safeguarding patient privacy. Conventional rule-based and machine learning approaches for deidentifying medical text face limitations with the linguistic complexity, variability, and context dependence inherent to clinical documentation. Recent advances in large language models (LLMs), combined with emerging quantum computing paradigms, present novel opportunities to enhance the accuracy, scalability, and resilience of health care data anonymization.