Journal of Medical Internet Research

Increasing demand on healthcare systems requires innovative and transformative solutions to deliver efficient, high-quality care. One promising approach is Digital Twin (DT) technology, which leverages real time data to create dynamic virtual representations of a physical entity (individuals or space) to anticipate future scenarios and support care decisions. While DTs have been explored in various sectors, their application in Hospital at Home (HaH), which delivers acute level care in home environments, remains unexplored.

The integration of social media into human subjects research offers significant opportunities for data collection, disease surveillance, and participant recruitment. However, it also poses a number of ethical challenges. This article evaluates the dual nature of social media as a research tool, highlighting its potential benefits while also addressing concerns about exacerbating health disparities, compromising participant privacy and confidentiality, challenging expectations around participant disclosure, and perpetuating discriminatory practices. By exploring issues related to equity and privacy, this article discusses the implications of digital recruitment and online behavioral advertising, underscoring the vital role of Institutional Review Boards (IRBs) in ensuring ethical standards are upheld. Furthermore, this work proposes key strategies for researchers and regulatory authorities, emphasizing community engagement, transparency, and inclusive recruitment practices. The analysis aims to guide stakeholders in navigating the ethical complexities of digital research, fostering transparency, trust, and accountability in the realm of human subjects research.

Early neurological deterioration (END) significantly worsens outcomes in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), yet clinicians lack reliable tools to identify high-risk patients who need intensified monitoring and preemptive interventions.

Health care providers must carefully monitor patients receiving long-term opioid therapy (LTOT) to minimize risks and maximize benefits. Yet, algorithms to support intervention during patient encounters are lacking, with accurate LTOT identification in routine care being the essential first step.

Complex and expanding datasets in clinical oncology applications require flexible and interactive visualization of patient data to provide physicians and other medical professionals with maximum amount of information. In particular, interdisciplinary tumor conferences profit from customized tools to integrate, link, and visualize relevant data from all professions involved.

Artificial intelligence (AI) techniques, particularly those employing machine learning (ML) and deep learning (DL) to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE) in prostate cancer.

While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, U.S. healthcare providers and hospital administrators have increasingly embraced ML, particularly through its integration with electronic health record (EHR) systems. This evolving landscape underscores the need for empirical evidence on ML adoption and its determinants; however, the relationship between hospital characteristics and ML integration within EHR systems remains insufficiently explored.

In France, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death among women. For around one third of women with hormone receptor-positive breast cancer, extending adjuvant endocrine therapy (AET) beyond the initial 5-year is now recommended to reduce the risk of recurrence and mortality. While weighing benefits against potential side effects is essential, little is known about how women seek information about extended AET or how they experience this extension.

As digital health technologies become increasingly integrated into health care systems worldwide, there is growing recognition that their full potential can be realized only when development is rooted in patient engagement (PE). Despite its proven value in clinical research and health care delivery, PE remains insufficiently embedded in digital health design and implementation. This perspective paper explores the current state of PE in digital health through findings from the Stakeholder Expectations Matrix program developed by Patient Focused Medicines Development. Drawing from 37 in-depth interviews across 6 key stakeholder groups, complemented by insights gathered during a multisession cocreation track at the Patient Engagement Open Forum, this paper highlights differing perspectives on digital health, the barriers to meaningful engagement, and the fragmented nature of data governance and technology adoption. Findings point not only to significant gaps in shared understanding, infrastructure, and policy but also to clear opportunities for collaboration, including early recommendations for building a more inclusive and patient-centered digital health ecosystem, one that supports sustainable innovation, trust, and systemwide impact.

The World Health Organization (WHO) plays a critical role in global health governance, but its popular legitimacy, a measure of public trust and support, has been contested, particularly during crises such as the COVID-19 pandemic. While legitimacy is widely studied through normative and elite-focused approaches, empirical assessments using public discourse remain limited. Social media platforms like X (formerly Twitter) offer real-time data for evaluating public sentiment toward the WHO.

Health research that uses predictive and/or generative AI is rapidly growing. Just as in traditional clinical studies, the way in which AI studies are conducted can introduce systematic errors. Transmission of this AI evidence into clinical practice and research needs critical appraisal tools for clinical decision makers and researchers.