Journal of Medical Internet Research
The leading peer-reviewed journal for digital medicine and health and health care in the internet age.
Editor-in-Chief:
Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada Rachele Hendricks-Sturrup, DHSc, MSc, MA, FACTS, Lead Editor; Research Director of Real-World Evidence, Duke-Margolis Institute for Health Policy, Washington, DC
Impact Factor 8.2 More information about Impact Factor CiteScore 10.4 More information about CiteScore
Recent Articles
Understanding how health-related rumor debunking evolves and spreads on social media is critical for public health communication and policy. Existing research, however, has been largely crisis-centered—dominated by studies of specific events, such as the COVID-19 pandemic—and offers limited insight into the longer-term patterns of thematic evolution, demographic targeting, and engagement dynamics of official debunking practices.
Internet-delivered cognitive behavioral therapies (iCBTs) can address the accessibility and affordability limitations of conventional therapy. Although iCBTs for social anxiety disorder (SAD) are efficacious, their effectiveness in routine care remains less well established. Helsinki University Hospital (HUS) provides a novel nationwide iCBT program for SAD.
To provide patients with gastric cancer with adequate health education information for effective overall management is crucial, while traditional manners exposed certain challenges. Conversational agents have increasingly been adopted for health care use to provide innovative solutions for patient education.
Systems based on large language models (LLMs), multimodal LLMs, and vision-language foundation models are increasingly being evaluated for medical report generation in imaging and related clinical workflows. Existing reviews have summarized technical architectures, radiology applications, readability, and benchmark performance, but clinical readiness remains uncertain because safety, human oversight, and workflow outcomes are sparsely and inconsistently reported.
Postoperative outcome assessment is often based on discrete follow-up visits, limiting characterization of individual recovery trajectories, and the timely identification of adverse events (AEs). Longitudinal smartphone-based monitoring may overcome these limitations by enabling frequent, resource-efficient collection of patient-reported outcomes and complications throughout recovery. Such data may provide a more patient-centered understanding of the postoperative course and complement conventional clinical surveillance.
Psychiatric distress is common among patients with cancer; yet, systematic interview-based screening remains difficult to scale in routine clinical care. Large language models (LLMs) have shown promise as scalable tools for mental health assessment, but most existing evidence is derived from clinician-authored records, translated text, or proxy data. The performance characteristics, error patterns, and explanatory behaviors of contemporary LLMs when applied to authentic, non-English psychiatric interviews remain insufficiently characterized.
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