Journal of Medical Internet Research

The incidence of adrenal incidentalomas is increasing annually, and most types of adrenal masses require surgical intervention. Accurate classification of common adrenal masses based on tumor computed tomography (CT) images by radiologists or clinicians requires extensive experience and is often challenging, which increases the workload of radiologists and leads to unnecessary adrenal surgeries. There is an urgent need for a fully automated, noninvasive, and precise approach for the identification and accurate classification of common adrenal masses.

Qualitative research is crucial for understanding the values and beliefs underlying individual experiences, emotions, and behaviors, particularly in social sciences and health care. Traditionally reliant on manual analysis by experienced researchers, this methodology requires significant time and effort. The advent of artificial intelligence (AI) technology, especially large language models such as ChatGPT (OpenAI), holds promise for enhancing qualitative data analysis. However, existing studies have predominantly focused on AI’s application to English-language datasets, leaving its applicability to non-English languages, particularly structurally and contextually complex languages such as Japanese, insufficiently explored.

The Extension for Community Healthcare Outcomes (ECHO) model was adapted for hypertension education of community health extension workers in the Federal Capital Territory, Nigeria, and delivered as a 7-part series.

Family caregivers provide essential care in the home to millions of individuals around the globe annually. However, family caregiving results in considerable burden, financial hardship, stress, and psychological morbidity. Identifying and managing stress in caregivers is important as they have a dual role in managing their own health as well as that of the person they care for. If stress becomes overwhelming, a caregiver may no longer be able to perform this essential role. Digital methods of stress monitoring may be 1 strategy for identifying effective interventions to relieve caregiver burden and stress.

Telemedicine has been associated with better cardiovascular outcomes, but its effects on the regression of mitral regurgitation (MR) and tricuspid regurgitation (TR) remain unknown.

Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. However, anti-VEGF has been reported to be associated with ROP reactivation. Therefore, an accurate prediction of reactivation after treatment is urgently needed.

The rapid progress in the development of artificial intelligence (AI) is having a substantial impact on health care (HC) delivery and the physician-patient interaction.

Embarking on a journey into the future of health care shaped by technological advances and the impact of the COVID-19 pandemic, we delve into the transformative landscape shaped by the integration of wearable technology, medically regulated devices, and advanced software. The ability to offer consumers unprecedented access to vital signs, advanced biomarkers, and environmental data enables a host of new capabilities to fill gaps in existing knowledge and permit individualized insights and education. Continuous monitoring enables individualized insights, emphasizing the need for a redefinition of health and human performance that is decentralized, dynamic, and personalized. The challenge lies in managing the massive amounts of continuous wearable data, necessitating new definitions of health data and secure practices. The COVID-19 pandemic has accelerated the adoption of digitalized consumer-facing diagnostics and software, transforming the traditional patient role. Consumers now have the tools to identify and understand an impending or existing disease state before they encounter traditional health care delivery health systems, making self-diagnosis commonplace. This shift empowers consumers to actively participate in their health, contributing to a new era where patients are in control of their well-being, from wellness to disease. Physicians in 2025 will engage with more informed and educated consumers, leveraging advanced analytic tools for diagnostics and streamlined patient management. Wearable devices play a pivotal role in enhancing patient engagement, while virtual reality and tailored software can be used by physicians to offer immersive learning experiences about conditions or upcoming procedures. Clinician decision support models and virtual care solutions will contribute to recruiting and maintaining health care providers amid a growing workforce shortage. Health care delivery organizations are transforming to improve outcomes at a lower cost, with partnerships with digital technology companies enabling innovative care models. This marks a historic moment where digital health and human performance solutions empower consumers to actively participate in their care. Physicians embrace digital tools, fostering richer patient partnerships, while health care organizations seize unprecedented opportunities for multilocation care delivery, addressing cost, workforce, and outcome challenges.

The successful implementation of the European Health Data Space (EHDS) for the secondary use of data (known as EHDS2) hinges on overcoming significant challenges, including the proper implementation of interoperability standards, harmonization of diverse national approaches to data governance, and the integration of rapidly evolving AI technologies. This work addresses these challenges by developing an interactive toolkit that leverages insights from 7 leading cancer research projects (Integration of Heterogeneous Data and Evidence towards Regulatory and HTA Acceptance [IDERHA], European Federation for Cancer Images [EUCAIM], Artificial intelligence Supporting Cancer Patients across Europe [ASCAPE], Personalised Health Monitoring and Decision Support Based On Artificial Intelligence and Holistic Health Records [iHelp], Central repository for digital pathology [Bigpicture], Piloting an infrastructure for the secondary use of health data [HealthData@EU] pilot, and improving cancer diagnosis and prediction with AI and big data [INCISIVE]) to guide in shaping the EHDS2 interoperability framework. Building upon the foundations laid by the Towards the European Health Data Space (TEHDAS) joint action (JA) and the new European Interoperability Framework (EIF), the toolkit incorporates several key innovative features. First, it provides interactive and user-friendly entry modules to support European projects in creating their own interoperability frameworks aligned with the evolving EHDS2 requirements technical and governance requirements. Second, it guides projects in navigating the complex landscape of health data standards, emphasizing the need for a balanced approach to implementing the EHDS2 recommended standards for data discoverability and sharing. Third, the toolkit fosters collaboration and knowledge sharing among projects by enabling them to share their experiences and best practices in implementing standards and addressing interoperability challenges. Finally, the toolkit recognizes the dynamic nature of the EHDS2 and the evolving regulatory landscape, including the impact of AI regulations and related standards. This allows for continuous adaptation and improvement, ensuring the toolkit remains relevant and useful for future projects. In collaboration with HSbooster.eu, the toolkit will be disseminated to a wider audience of projects and experts, facilitating broader feedback and continuous improvement. This collaborative approach will foster harmonized standards implementation across projects that ultimately contribute to the development of a common EHDS2 interoperability framework.

Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients’ conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are often limited by their inability to handle irregular sampling and missing values, and many lack rigorous external validation across institutions.

Electronic health record (EHR) data are anticipated to inform the development of health policy systems across countries and furnish valuable insights for the advancement of health and medical technology. As the current paradigm of clinical research is shifting toward data centricity, the utilization of health care data is increasingly emphasized.

Although mobile health (mHealth) interventions have demonstrated effectiveness in modifying 1 or 2 health-risk behaviors at a time, there is a knowledge gap regarding the effects of stand-alone mHealth interventions on multiple health risk behaviors.