Journal of Medical Internet Research

Online health communities (OHCs) provide vital peer support and health information to individuals managing chronic conditions. However, sustained user engagement remains challenging, with many users reducing activity over time despite the ongoing benefits these platforms offer. While some individuals may improve or become more knowledgeable, sustained engagement remains critical because ongoing participation fosters trust, peer support, and continuous access to evolving health information that may persist beyond initial recovery or learning. Hence, it is important to consider how community needs evolve as users’ health needs and information-seeking behaviors change to inform how we support users through different stages of their health and participation journeys.

No measurement, no understanding; no understanding, no control: this foundational scientific principle was exposed as a public health dysfunction by the COVID-19 pandemic. Transmission chains spread invisibly, and the contact histories, mobility patterns, and biosignals necessary for control were never systematically collected. Although sensors and digital technologies existed, the fundamental reason measurement failed was the absence of privacy infrastructure that would have enabled people to provide data with confidence. This failure had structural reasons. The object of measurement in infectious disease control is not a physical phenomenon but human beings, and measurement therefore enters the core of privacy: contact histories, social relationships, and bodily states. Because greater precision also deepens privacy intrusion, contact-tracing apps faced 2 failures: privacy-centered designs lost epidemiological utility, while utility-centered designs were rejected through public distrust. Neither achieved sufficient measurement. This Viewpoint reframes the problem. Privacy protection is not a constraint that impedes infectious disease control but the enabling condition upon which effective measurement depends. Existing regulations and technical methods have not been designed from this premise and have therefore failed to break the cycle of structural distrust. As an institutional approach to filling this gap, we present VRAIO (verifiable record of AI output), which integrates democratic rule-setting, metadata declaration, third-party verification, tamper-proof ledgers, and violation-deterrence incentives. Once privacy infrastructure is established, this foundational principle can operate freely in infectious disease control for the first time. It will enable high-resolution epidemiology and precision intervention, opening a new path for public health that reconciles infection control with individual autonomy and social freedom without relying on blanket social shutdowns.

Self-directed digital mental health programs can increase access to evidence-based interventions for child anxiety. However, there has been little comprehensive investigation of real-world dissemination, especially using implementation science approaches.

Intrinsic capacity (IC) has become a central concept in healthy aging because it emphasizes functional ability across the aging trajectory rather than disease alone. However, current IC assessment primarily relies on episodic clinical evaluations, which are insufficient for continuous monitoring and early identification of functional decline. Recent advances in AI and digital health technologies have created new opportunities for objective, continuous, and real-world assessment of IC. However, existing evidence remains fragmented across AI-enabled devices, digital biomarkers (DBs), AI techniques, and IC domains.

Although pharmacotherapy is the primary treatment for patients with acute-phase panic disorder, its incomplete efficacy causes them to experience frequent panic attacks and severe anticipatory anxiety for a considerable period. A prescription-based mobile digital therapeutic (DTx) that integrates self-guided cognitive behavioral therapy (CBT), real-time symptom management, and lifestyle tracking can be used as an adjunct to pharmacotherapy for these patients, helping them achieve rapid symptom recovery.

Cervical degenerative diseases are a global public health issue, and their incidence is rising worldwide. Although an increasing number of studies on traditional machine learning (TML) and deep learning (DL) have been conducted in the detection and segmentation of cervical degenerative diseases and have reported promising task-specific results, the performance of these models has not yet been systematically analyzed.

The rapid diffusion of generative AI is transforming health care delivery, education, administration, and research. In response, health care organizations have invested heavily in AI literacy initiatives and workforce development programs. Existing frameworks primarily focus on whether health care professionals understand AI and whether they can engage with AI effectively. However, health care practice increasingly reveals that individuals with similar levels of AI literacy and AI engagement often contribute very differently to AI-enabled work and organizational adoption. Some professionals primarily use AI to improve their own work, whereas others facilitate AI adoption, coordinate stakeholders, and integrate AI into routine practice. This observation suggests that current perspectives may overlook an important dimension of workforce AI enablement. In this Viewpoint, we argue that AI literacy and AI engagement alone provide an incomplete explanation of how health care organizations realize the benefits of AI. Drawing upon literature from AI literacy, fluency theory, human-AI interaction, innovation diffusion, implementation science, and health care workforce development, we propose the health care workforce AI enablement matrix (HWAEM). HWAEM conceptualizes workforce AI enablement through 2 complementary capabilities: AI fluency and AI harnessing. AI fluency refers to the capability to engage with AI effectively, appropriately, and responsibly across professional contexts, whereas AI harnessing refers to the capability to identify opportunities for AI-enabled improvement, mobilize stakeholders, facilitate adoption, and integrate AI into collective work practices. The interaction of these capabilities generates 4 workforce profiles: AI novices, AI practitioners, AI facilitators, and AI leaders. Through HWAEM, this Viewpoint argues that health care workforce AI enablement is better understood through the complementary capabilities of AI fluency and AI harnessing than through AI literacy and AI engagement alone. We illustrate how these profiles manifest in health care practice and discuss implications for workforce development and AI implementation. HWAEM offers a new perspective for understanding health care workforce preparedness in the generative AI era and provides a foundation for future empirical research.

Emergency departments (EDs) face persistent challenges related to overcrowding, boarding, ambulatory care access barriers, and workforce strain, contributing to compromised patient care and high rates of physician burnout. Virtual care has emerged as a potential strategy to alleviate pressure on emergency care systems. In 2020, the Veterans Health Administration (VA) launched the national Tele-Emergency Care (TEC) program, in which patients who call a call center can be connected to an emergency medicine clinician by phone or video. Although virtual care may help address ED capacity and clinician burnout, the perspectives of emergency medicine–trained clinicians remain limited.

AI is increasingly incorporated into psychiatric triage, risk prediction, passive monitoring, clinical documentation, and patient-facing conversational systems. These applications may improve access, continuity, efficiency, and pattern recognition, but they also redistribute epistemic authority and complicate responsibility when harm occurs. European regulation is developed in relation to market access, data governance, risk management, and product safety, yet remains fragmented regarding civil liability, organizational negligence, and the psychiatric standard of care. This Viewpoint examines how liability and standard of care should be understood when AI becomes part of psychiatric reasoning in Europe. It advances one central thesis: psychiatric AI requires justified integration supported by layered accountability within, but not determined by, European regulation. It presents a targeted doctrinal and normative synthesis of binding European Union instruments, regulatory guidance, selected national governance materials, and psychiatric, bioethical, legal, and digital mental health literature. It distinguishes binding law from guidance and policy, and separates ex ante regulation from ex post liability, and from professional standards of care. Four illustrative domains are analyzed: conversational or therapeutic chatbots, suicide prediction, digital phenotyping and passive monitoring, and large language model documentation. Psychiatric AI raises distinctive concerns because psychiatric judgment depends heavily on testimony, contextual meaning, therapeutic trust, risk interpretation, privacy, and liberty-sensitive decisions. Existing European instruments, including the AI Act, Medical Device Regulation, General Data Protection Regulation, revised Product Liability Directive, and European Health Data Space Regulation, establish governance duties, but do not provide a harmonized fault-based liability framework for AI-assisted health care. Regulatory compliance may inform later legal assessment, but it does not determine whether psychiatric care was reasonable. The proposed standard of justified integration requires knowledge of intended use and model limits, assessment of local and patient-level applicability, active clinical interpretation, disclosure when AI use is material to consent or trust, documentation in high-stakes decisions, and organizational audit. Accountability should be distributed across developers, deployers, and clinicians according to control and preventability. Mixed-fault scenarios are therefore likely to be common. The augmented-clinician model and layered accountability are offered as normative proposals rather than settled European legal standards. Clinicians should remain responsible for contextual, patient-centered judgment; developers for design, validation, documentation, and foreseeable misuse; and deployers for procurement, training, workflow integration, local validation, monitoring, and escalation. Future empirical research should evaluate effects on clinician reliance, documentation burden, patient outcomes, coercive interventions, therapeutic trust, and feasibility across differently resourced services.

In a diagnostic study of 104 western blot and subcutaneous xenograft tumor images, a high-fidelity generative model produced forgeries that could alter the conclusions of a study; 24 PhD-level expert reviewers could not reliably distinguish the forgeries from authentic figures (mean accuracy 50.5%, SD 6.9%), while the best-performing commercial AI detector achieved only moderate discrimination (area under the curve 0.790, 95% CI 0.695-0.885), revealing critical vulnerabilities in current research-integrity safeguards.

Social media platforms, particularly YouTube (Google LLC), are important sources of health information, but also significant vectors for misinformation and pseudoscience. While many studies analyze the content and sentiment of this information, the dynamic, sequential nature of user interactions, which can shape belief formation and community dynamics, remains poorly understood.