Journal of Medical Internet Research

Telemedicine has expanded rapidly in recent years, with particularly pronounced growth following the COVID-19 pandemic. By improving access to care and offering greater flexibility in service delivery, it has become an important component of health care. Although the benefits of telemedicine for patients are well documented, its effects on physician job satisfaction remain insufficiently understood. Given the importance of job satisfaction for workforce stability, physician well-being, and quality of care, further examination of how telemedicine affects physician job satisfaction is warranted.

Carbohydrate counting (CC) assists people with type 1 diabetes (T1D) adjust mealtime insulin doses; however, it is often burdensome. Mobile apps can simplify this process by automating carbohydrate estimation and insulin calculations, yet no comprehensive solution currently combines photo-based carbohydrate recognition with an integrated bolus calculator.

Medical applications of mathematical modeling, including machine learning models, knowledge graphs, and health digital twins, primarily involve the prediction of patient outcomes. This expert perspective examines how mathematical modeling can contribute to health care quality management. Definitions of procedures, patient outcomes, and quality metrics are provided with a quantitative focus. The emphasis is subsequently placed on 3 categories of patient-centered quality of care, namely, patient safety, procedure accuracy, and procedure efficacy, for which a conceptual and mathematical description is provided. Different levels of modeling tasks essential for managing patient-centered quality of care are identified. This article facilitates a deeper understanding of the topic by assigning relevant publications to these 3 quality categories. Focus is placed on the applicability of graph-based methods, including knowledge graphs and health digital twins, to improve quality management in health care. We have presented a clinical scenario and provided information on methodological limitations, future research directions, and practical implications.

Rapidly and accurately synthesizing large volumes of evidence is a time- and resource-intensive process. Once published, reviews often risk becoming outdated, limiting their usefulness for decision makers. Recent advancements in artificial intelligence (AI) have enabled researchers to automate stages of the evidence synthesis process, from literature searching and screening to data extraction and analysis. As previous reviews on this topic have been published, a significant number of tools have been further developed and evaluated. Furthermore, as generative AI increasingly automates evidence synthesis, understanding how it is studied and applied is crucial, given both its benefits and risks.

Early in the children’s COVID-19 rollout in the United States, racial and ethnic vaccination rate disparities were evident. Based on COVID-19 communication literature and qualitative interviews with Hispanic parents, we developed a mobile phone–delivered digital intervention to address factors associated with low vaccine confidence.

Medication adherence among patients with cancer is generally low. Mobile health (mHealth) has gradually been applied to improve this situation, but systematic evidence of its effectiveness remains lacking.

The detection of pulmonary nodules (PNs) has increased with the use of low-dose computed tomography screening. Effective management requires timely longitudinal surveillance and reliable comparison with prior examinations, yet access to previous imaging across institutions is often fragmented, leading to delays and potentially unnecessary repeat scans and costs. Cloud-based medical imaging (CMI) solutions offer a potential means of improving access and facilitating cross-institutional data exchange. However, the adoption and utility of CMI in PN care, especially in China, remain underexplored.

The identification and management of depression during pregnancy is an important public health issue. Although many existing psychological intervention programs are effective, their implementation is plagued by issues, such as insufficient professional resources and lengthy intervention cycles. Studies have suggested that internet-based problem management plus (IPM+) can effectively address the aforementioned challenges in the management of general depression. However, its application in the pregnant population remains to be verified.

Artificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiologists’ efficiency. In China, growing imaging demand and workforce shortages have placed increasing pressure on radiology services. However, evidence on the operational impact of AI on reporting efficiency remains limited.