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
Impact Factor 5.8 CiteScore 14.4
Recent Articles
![Electronic Health Record–Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study Article Thumbnail](https://asset.jmir.pub/assets/a38b7a9b7efb8a015f8dd10a9619f3ba.png 480w,https://asset.jmir.pub/assets/a38b7a9b7efb8a015f8dd10a9619f3ba.png 960w,https://asset.jmir.pub/assets/a38b7a9b7efb8a015f8dd10a9619f3ba.png 1920w,https://asset.jmir.pub/assets/a38b7a9b7efb8a015f8dd10a9619f3ba.png 2500w)
The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security.
![Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review Article Thumbnail](https://asset.jmir.pub/assets/36fa09c9f7c2012c39af6ad70181ed0b.png 480w,https://asset.jmir.pub/assets/36fa09c9f7c2012c39af6ad70181ed0b.png 960w,https://asset.jmir.pub/assets/36fa09c9f7c2012c39af6ad70181ed0b.png 1920w,https://asset.jmir.pub/assets/36fa09c9f7c2012c39af6ad70181ed0b.png 2500w)
In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.
![Self-Management Using eHealth Technologies for Liver Transplant Recipients: Scoping Review Article Thumbnail](https://asset.jmir.pub/assets/de8efb6e9fd65756982b90585170464c.png 480w,https://asset.jmir.pub/assets/de8efb6e9fd65756982b90585170464c.png 960w,https://asset.jmir.pub/assets/de8efb6e9fd65756982b90585170464c.png 1920w,https://asset.jmir.pub/assets/de8efb6e9fd65756982b90585170464c.png 2500w)
Liver transplantation has become increasingly common as a last-resort treatment for end-stage liver diseases and liver cancer, with continually improving success rates and long-term survival rates. Nevertheless, liver transplant recipients face lifelong challenges in self-management, including immunosuppressant therapy, lifestyle adjustments, and navigating complex health care systems. eHealth technologies hold the potential to aid and optimize self-management outcomes, but their adoption has been slow in this population due to the complexity of post–liver transplant management.
![Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis Article Thumbnail](https://asset.jmir.pub/assets/8d638371186b76518a1e717f86c5a858.png 480w,https://asset.jmir.pub/assets/8d638371186b76518a1e717f86c5a858.png 960w,https://asset.jmir.pub/assets/8d638371186b76518a1e717f86c5a858.png 1920w,https://asset.jmir.pub/assets/8d638371186b76518a1e717f86c5a858.png 2500w)
![Collaborative Human–Computer Vision Operative Video Analysis Algorithm for Analyzing Surgical Fluency and Surgical Interruptions in Endonasal Endoscopic Pituitary Surgery: Cohort Study Article Thumbnail](https://asset.jmir.pub/assets/3749b38c61051abc4bcd62db2e39ca9c.png 480w,https://asset.jmir.pub/assets/3749b38c61051abc4bcd62db2e39ca9c.png 960w,https://asset.jmir.pub/assets/3749b38c61051abc4bcd62db2e39ca9c.png 1920w,https://asset.jmir.pub/assets/3749b38c61051abc4bcd62db2e39ca9c.png 2500w)
The endonasal endoscopic approach (EEA) is effective for pituitary adenoma resection. However, manual review of operative videos is time-consuming. The application of a computer vision (CV) algorithm could potentially reduce the time required for operative video review and facilitate the training of surgeons to overcome the learning curve of EEA.
![Design of Digital Mental Health Platforms for Family Member Cocompletion: Scoping Review Article Thumbnail](https://asset.jmir.pub/assets/09d8f1e13cf75a9b223ae346b328ba43.png 480w,https://asset.jmir.pub/assets/09d8f1e13cf75a9b223ae346b328ba43.png 960w,https://asset.jmir.pub/assets/09d8f1e13cf75a9b223ae346b328ba43.png 1920w,https://asset.jmir.pub/assets/09d8f1e13cf75a9b223ae346b328ba43.png 2500w)
The COVID-19 pandemic placed an additional mental health burden on individuals and families, resulting in widespread service access problems. Digital mental health interventions suggest promise for improved accessibility. Recent reviews have shown emerging evidence for individual use and early evidence for multiusers. However, attrition rates remain high for digital mental health interventions, and additional complexities exist when engaging multiple family members together.
![Digital Interventions to Modify Skin Cancer Risk Behaviors in a National Sample of Young Adults: Randomized Controlled Trial Article Thumbnail](https://asset.jmir.pub/assets/5000f2b1ccb69b7e7c648dca4dd07ce2.png 480w,https://asset.jmir.pub/assets/5000f2b1ccb69b7e7c648dca4dd07ce2.png 960w,https://asset.jmir.pub/assets/5000f2b1ccb69b7e7c648dca4dd07ce2.png 1920w,https://asset.jmir.pub/assets/5000f2b1ccb69b7e7c648dca4dd07ce2.png 2500w)
![Issues Related to the Use of Visual Social Networks and Perceived Usefulness of Social Media Literacy During the Recovery Phase: Qualitative Research Among Girls With Eating Disorders Article Thumbnail](https://asset.jmir.pub/assets/fad946213e5be177686673f3908d697d.png 480w,https://asset.jmir.pub/assets/fad946213e5be177686673f3908d697d.png 960w,https://asset.jmir.pub/assets/fad946213e5be177686673f3908d697d.png 1920w,https://asset.jmir.pub/assets/fad946213e5be177686673f3908d697d.png 2500w)
The patient-centered approach is essential for quality health care and patient safety. Understanding the service user’s perspective on the factors maintaining the health problem is crucial for successful treatment, especially for patients who do not recognize their condition as clinically relevant or concerning. Despite the association between intensive use of visual social media and body dissatisfaction and eating disorders, little is known about the meanings users assign to posting or searching for edited photos and the strategies they use to protect themselves from digital risks.
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