Journal of Medical Internet Research

Binge eating disorder (BED) is highly prevalent and impairing; yet, the UK national guideline–recommended first-line treatment of guided self-help (ie, supported program-led interventions in which content is delivered by the program with brief support) remains underused in routine National Health Service (NHS) care. Digital delivery offers a scalable approach, but evidence from real-world NHS settings is limited.

Acute respiratory infections caused by influenza, respiratory syncytial virus (RSV), and SARS-CoV-2 remain a major public health challenge in Europe. Although surveillance systems for these pathogens are well established, the past 2 decades have seen a rapid diversification of data streams supporting surveillance and research. This expanding data landscape, combined with fragmentation across institutions, sectors, and countries, may limit timely evidence synthesis and effective public health decision-making.

Colorectal cancer (CRC) screening relies on structured risk assessment and guideline-concordant communication, which remain challenging to implement consistently in real-world practice. Digital tools based on large language models (LLMs) may support such workflows, but their feasibility and safety in structured screening contexts have not been well evaluated.

Malawi was a pioneer among African countries in implementing a coordinated, government-led effort to streamline COVID-19 support using digital health tools. In response to the pandemic, a COVID-19 WhatsApp chatbot was developed to support the public with information, symptom reporting, and service navigation during the pandemic.

eHealth interventions have demonstrated potential to address challenges related to health and the health care system in low- and middle-income countries. To effectively leverage eHealth in supporting health care in Ethiopia, the assessment and development of the eHealth literacy of patients are essential.

AI has entered the wellness space through apps and wearables. These technologies can collect real-time data, infer lifestyle patterns, and dynamically generate nutrition and exercise recommendations. Generative AI personalizes diet and activity information, encouraging behavior change. The objective of this Viewpoint is to explore the potential medium-term consequences of AI integration in diet and exercise apps from an end-user perspective. We applied a foresight methodology—the Futures Wheel (FW)—and defined its central trend as the growing integration of AI into consumer wellness platforms. The analysis outlines seven first-order consequences: (1) personalization of nutrition and fitness plans, (2) 24/7 health coaching, (3) integration with smart technology, (4) increased privacy and surveillance concerns, (5) data-driven risk profiling and moral hazard, (6) incorporation into organizational processes, and (7) acceleration of health inequalities driven by the digital divide. Second-order consequences included potential improvements in health outcomes and health literacy, as well as risks of privacy erosion, algorithmic bias, behavior-linked underwriting models, deskilling of health and fitness professionals, and shifts in food and exercise culture toward more individualized, and potentially isolating practices. Cross-cutting patterns highlighted recurring trade-offs between personalization and surveillance, scalability and user agency, and optimization and equity. Wellness practice will expand along with AI’s ability to personalize recommendations, automate behaviors, and engage users. AI wellness popularization is promising for chronic disease prevention and health optimization. The FW reveals that the depth of adaptation will be determined by the implementation of changes at the levels of technology, user behavior, infrastructure, and legal and ethical frameworks.

Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical data into patient-friendly narratives by incorporating longitudinal comparisons with prior examinations.

Just-in-time adaptive interventions (JITAIs) use real-time data to deliver personalized support at moments of heightened need and may improve dietary behaviors in real-world settings.

Large language models (LLMs) are increasingly embedded in clinical and population health workflows, including conversational agents such as health chatbots. As chatbots evolve from rule-based approaches to hybrid and LLM-enabled designs, risks and concerns about deployment readiness shift. Unlike rule-based chatbots, LLM outputs can be unpredictable, error-prone, and difficult to validate with traditional evaluation methods. Public health teams integrating customized LLMs into interventions face practical and ethical challenges related to performance variability, uncertainties about model behaviors, and inequitable performance across languages. Although existing frameworks address domains such as safety, ethics, effectiveness, engagement, and implementation, they often assume or imply—rather than operationalize—an explicit benchmark for deployment and implementation decisions. We propose an acceptance criteria framework (ACF) to determine implementation fit, defined as meeting prespecified minimum performance standards and demonstrating nonproblematic behavior under anticipated use. The ACF uses project-relevant and off-topic prompts, structured expert review, and prespecified thresholds to produce a documented decision record that can be iteratively rerun after model revisions. We demonstrate the framework through a case application in a tobacco cessation text messaging intervention, illustrating how the ACF can guide deployment decisions.

Digital technologies are becoming an important part of health care, including for individuals with attention-deficit/hyperactivity disorder (ADHD). Digital health innovations present valuable opportunities to provide flexible and tailored support for their diverse needs, along with significant challenges. Attentional, organizational, and motivational characteristics associated with ADHD may affect how individuals engage with digital tools. Potential risks include additional access barriers, the exclusion of underserved groups, and diminished quality of care. To help reduce these risks, the development, evaluation, and implementation of digital tools must be person-centered and guided by a comprehensive understanding of the diverse needs of all stakeholders.