Journal of Medical Internet Research

Meyer-Keirath and colleagues found no significant difference in posttreatment symptoms between video-based and face-to-face psychotherapy. However, the wide confidence interval indicates substantial imprecision, while the prediction interval suggests that comparative effects may vary considerably across settings; neither supports a conclusion of equivalence. For health services, symptom outcomes alone tell only part of the story. Future studies should report, by randomized group, who starts treatment, who discontinues it, and who completes the final assessment. These are distinct outcomes that may differ between delivery formats and affect the interpretation of symptom results. Video-based psychotherapy is a reasonable option, but its value should be assessed across the full treatment pathway. Adequately powered noninferiority trials should identify which patients can access, remain in, and benefit from each format.

Oral pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, but its real-world impact depends on sustained adherence. Long-acting injectable cabotegravir (CAB-LA) may reduce the burden of daily or event-driven pill use. In China, many oral PrEP users obtain medication online, but little is known about their prior CAB-LA awareness or willingness to consider it.

Electronic patient-reported outcomes (ePROs) can support patient-centered oncology care, but in early breast cancer, it is unclear whether real-time ePRO-triggered triage adds benefit beyond lower-intensity app-based support and scheduled ePRO collection.

Digital media exposure is nearly universal by the preschool years. However, little is known about the timing of first exposure to different devices, and the factors associated with earlier exposure.

In this viewpoint, we present our perspective—informed by published evidence and by our own work on AI-driven digital health systems—on why AI has had limited real-world impact in preventive health and how it should be redesigned. Most AI systems in health care are built as episodic prediction tools, whereas preventive care depends on sustained behavioral change; the consequences are visible in consistently high attrition and low long-term adherence across digital health interventions. The aim of this viewpoint is to argue that this failure is structural rather than incremental and to propose an integrated, interaction-centric alternative. We advance 3 key messages. First, the limited impact of AI in preventive health reflects a structural misalignment between episodic, prediction-centric system design and the longitudinal dynamics of health behavior. Second, addressing this misalignment requires treating continuous interaction as the primary unit of design, which we articulate through a behavioral informatics framework integrating 6 interdependent layers: data, knowledge, reasoning, interaction, behavior, and governance. The contribution of this framework is explicitly integrative: it synthesizes previously separate strands, including just-in-time adaptive interventions, behavioral intervention technologies, and human-AI interaction research, into a single system-level account. Third, evaluation must shift from predictive accuracy and short-term engagement toward measurable behavioral trajectories and longitudinal outcomes, with explicit attention to feasibility and to conditions under which simpler, non-AI interventions remain preferable. This viewpoint is addressed to digital health researchers and AI developers, behavioral scientists, clinicians involved in preventive care, and policymakers responsible for governing continuously interacting AI systems.

Generative AI (GenAI) is being deployed rapidly in medicine, and evidence of its clinical safety and efficacy depends on transparent reporting of randomized controlled trials (RCTs). The CONSORT-AI (Consolidated Standards of Reporting Trials–AI) extension is a benchmark for AI trial reporting, but it was drafted for conventional AI, and its applicability to GenAI has not been examined.

Over 55 million people worldwide are living with dementia, resulting in significant caregiving demands and caregiver burden. Socially assistive robots (SARs) are increasingly being explored to reduce caregiving workload and improve the quality of life for persons with dementia; however, their effectiveness depends on person-centered design grounded in a comprehensive understanding of the target population. Personas and their corresponding daily activity maps (DAMs) are user-centered design tools that provide a holistic understanding of the personal, medical, emotional, and life trajectory of persons with dementia, enabling SARs to engage in contextually relevant and empathetic interactions. Despite their potential, these tools have not been formally evaluated for dementia care and digital health technology development.

Radiology reports serve as the definitive clinical record of imaging diagnoses, with their accuracy being directly critical to patient safety. However, amid escalating workloads, manual review remains inadequate for capturing subtle errors in reports on a large scale. Although large language models (LLMs) have demonstrated considerable promise in medical text processing, previous studies have predominantly been confined to radiology report text from a single imaging modality or datasets with synthetic errors.

Traumatic knee injuries (TKI) are common and are associated with a 4 to 6 times increased risk of posttraumatic knee osteoarthritis (PTOAK) over the subsequent 15- to 20-year period. There is clear evidence that the risk can be reduced, as per the 2022 OPTIKNEE recommendations, but long-term care availability is limited. Digital health interventions (DHIs), such as wearable devices, telehealth innovations, and mobile apps, have the potential to deliver long-term musculoskeletal care. DHIs can be scaled up to support health care delivery by improving efficiency and accessibility.

There is currently scarce information about the cost-effectiveness of interventions for individuals with chronic whiplash-associated disorders (WAD) or of internet-based exercise programs for neck pain, including WAD.

AI is increasingly used in long-term care (LTC), yet reliability is often judged at the level of individual models or devices. This is insufficient in LTC, where residents vary in capacity, autonomy, and preferences. Their ability to question AI outputs may fluctuate, and care delivery depends on distributed roles and finite operational capacity. We define sociotechnical reliability as the capacity of an AI-enabled care system to consistently translate resident-relevant information into appropriate and timely care actions under real-world LTC conditions, with failures that are detectable and correctable across the care pathway. In this Viewpoint, we draw on professional experience overseeing AI initiatives across LTC and hospital settings, examined in relation to a purposively mapped interdisciplinary literature, to inform the development of a reliability framework. Our aim is to reframe AI reliability in LTC as an end-to-end care system property and to provide a framework for identifying where reliability is gained, lost, and evaluated. We propose 5 reliability functions: sensing, inference, verification and contextualization, response and escalation, and learning and adaptation, with governance and accountability operating across all 5 functions. The framework highlights recurrent failures, including incomplete sensing, nonportable inference, overreliance on or dismissal of AI outputs during verification, unclear ownership or delayed response, and repeated failures without correction. The framework also makes reliability empirically examinable through questions about where the pathway fails, who has the authority to verify and act, whether response demands are sustainable within available capacity, and whether failures become visible to governance and inform learning and adaptation. Our central viewpoint is that AI in LTC should be judged not by model performance alone but by whether the surrounding care system can reliably translate AI-generated information into care.

Traditional influenza surveillance systems rely on sentinel hospitals and often experience a 1‐ to 2-week reporting lag, limiting timely public health response. Social media platforms provide real-time data streams, but extracting reliable epidemiological signals remains challenging due to substantial linguistic noise. Although large language models (LLMs) improve semantic extraction, their computational cost limits routine deployment in resource-constrained public health settings.