Journal of Medical Internet Research

Traditional influenza surveillance systems rely on sentinel hospitals and often experience a 1‐ to 2-week reporting lag, limiting timely public health response. Social media platforms provide real-time data streams, but extracting reliable epidemiological signals remains challenging due to substantial linguistic noise. Although large language models (LLMs) improve semantic extraction, their computational cost limits routine deployment in resource-constrained public health settings.

Patients with infertility often have inadequate reproductive health literacy (RHL), while existing health education materials may be difficult to read, overly complex, and insufficiently tailored to individual needs. In recent years, large language models (LLMs) have created new opportunities to improve health education through their ability to generate and interpret medical information. However, their applicability and content quality in infertility education remain insufficiently evaluated.

The rapid adoption of generative AI in the community and health care necessitates reliable, human-led evaluation.

Health care systems increasingly promote patient engagement and self-management through digital health services. Digital care pathways, which support coordinated care delivery across the patient journey, have emerged as a promising approach, although evidence regarding their impact on resource use and costs remains limited.

Large language models (LLMs) are increasingly applied to causal explanation in public health and epidemiology. However, recent studies suggest that LLMs exhibit instability in processing temporal information, which raises concerns about their suitability for generating coherent causal explanations. Despite this, little is known about how distinct components of temporal information influence LLM-generated causal explanations in epidemiological contexts.

The accurate detection of clinical symptoms in electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal studies in psychiatry. Free-text clinical notes are an essential source of symptom-level information, particularly in psychiatry; however, the automated extraction of symptoms from clinical text remains challenging.

Screen use is almost universal, and numerous studies have highlighted both the benefits and the risks of this digital integration. However, evidence on the association between screen time and health outcomes among university students remains inconsistent. Substantial methodological variation has limited the synthesis and interpretation of existing findings.

Dementia is a growing health burden worldwide; however, scalable, low-cost strategies for early risk identification are lacking. Because cardiac and cognitive aging are closely associated, a routinely collected 12-lead electrocardiogram (ECG), interpreted using foundation model embeddings, is a promising and accessible candidate marker. However, it is unclear whether such embeddings carry predictive information independent of chronological age—the dominant risk factor for dementia—and whether the underlying changes follow a detectable prediagnostic trajectory.

The ethics of autonomous clinical AI are controversial. Traditional ethics assessments have focused on AI model performance rather than health system outcomes. Nichol and colleagues translate established research ethics principles into 9 practical recommendations for trials of autonomous clinical AI. Their findings support an evolution in clinical trial design. The accuracy and performance of the model is important, but the ethical unit of evaluation should be the complete care pathway, with prespecified obligations for subgroup performance, follow-up care, system failures, and model change.

A central concern in ecological momentary assessment (EMA) research is whether repeated self-report alters the reporting process itself. In most EMA domains, longitudinal stability of reporting is assumed but rarely tested.