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 6.0 More information about Impact Factor CiteScore 11.7 More information about CiteScore
Recent Articles
Federal regulations require that laboratory test results be released to patients through online portals in near–real time, often before clinicians review or contextualize them. While these policies expand transparency and affirm patients’ rights to their health information, access alone does not ensure that patients can make sense of their results or determine when and how to act. How regulatory mandates for transparency translate into appropriate engagement remains poorly understood.
Dementia is on the rise globally due to increasing life expectancies and population growth. Digital technologies may help detect early signs, enabling timely interventions to slow or reverse cognitive decline. However, to support the successful implementation of these digital technologies into health care settings, they must be acceptable to target users. Older adults and those with mild cognitive impairment (MCI) are at risk of developing dementia in later life and need to be able to use these technologies in order for this intervention to be approved and implemented in clinical practice.
Reducing 30-day hospital readmissions has been a long-standing goal across health systems in the United States. While nurse-led phone outreach has been widely adopted to support transitional care, its reach is constrained by staffing and time limitations. Mobile health (mHealth) interventions, such as automated SMS text messaging and patient portals, offer scalable alternatives but have shown mixed effectiveness in reducing readmissions. Understanding how patients engage with mHealth after discharge may help optimize these tools for postdischarge care.
This evaluation of 36,000 clinical vignettes found that next-generation reasoning large language models, o3-mini and DeepSeek-R1, frequently perpetuate racial and gender stereotypes for common medical conditions, indicating that advancements in reasoning do not inherently improve representational fairness.
Phenotype-driven prenatal diagnosis relies on the precise correlation between ultrasound findings and genetic outcomes; however, this process is hindered by the unstructured nature of clinical ultrasound reports. While large language models (LLMs) hold the potential to address this challenge, their specific application in this domain remains systematically underexplored.
Preprints Open for Peer Review
Open Peer Review Period:
-
Open Peer Review Period:
-
Open Peer Review Period:
-
We are working in partnership with
This journal is indexed in
