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
Traditionally, the number and location of cerebral microbleeds (CMBs) are manually calculated based on magnetic resonance imaging (MRI) characteristics such as shape, size, and signal features. Although accurate, manual detection requires expert interpretation and is costly. Therefore, it is necessary to explore an effective auxiliary detection method. In recent years, deep learning (DL) has been increasingly used in the detection of cerebral hemorrhage. Some studies have explored image-based DL models for diagnosing CMBs. Nevertheless, systematic evidence regarding their diagnostic accuracy is lacking.
The integration of digital health technology (DHT) into chronic kidney disease (CKD) care holds transformative potential for enhancing patient self-management and slowing disease progression. Despite the growing availability of DHT, there remains limited understanding of the factors that facilitate or hinder their adoption and use among patients with CKD.
Online health communities are crucial resources for individuals managing socially stigmatized and physically burdensome conditions, such as hemorrhoids. These anonymous forums provide critical avenues for emotional validation and information exchange that may be underrepresented or difficult to access in traditional clinical settings. Despite their growing importance as data sources for understanding patient experiences, large-scale longitudinal analyses of the thematic and emotional evolution within such communities remain scarce.
Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and show low fact-level explainability, limiting clinical adoption and regulatory compliance. Existing approaches, such as retrieval-augmented generation, partially address these issues by grounding answers in source documents; however, the aforementioned problems persist.
Electronic health records are a central component of digital health strategies worldwide. Early implementation phases are particularly critical for shaping long-term adoption patterns but remain insufficiently studied, especially in large-scale, policy-driven rollouts. In January 2025, Germany introduced the electronic patient record (, ePA) as a nationwide opt-out system, creating a unique opportunity to examine early implementation in routine outpatient care.
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