Journal of Medical Internet Research

Hospitals continue to invest heavily to increase their level of digitalization. While advanced digital maturity is assumed to improve hospital performance, empirical evidence remains mixed. This tension is mirrored by the productivity paradox of IT, whereby investments in digital technologies do not consistently translate into observable performance gains.

Internet search engines serve as primary gateways to cancer information; yet, the commercialization of health content within organic search results remains understudied. While covert promotional content—such as native advertising and stealth marketing—has been documented in various contexts, systematic comparisons across structurally divergent search platforms are lacking.

Traditional research ethics governance was designed for bounded protocols, identifiable investigators, and temporally limited encounters with human participants. Data science health research (DSHR) disrupts that architecture because health data, biological materials, computational representations, and models persist, travel, combine, and acquire new uses across time. In this viewpoint, we use DSHR broadly to include research using large health datasets and adjacent secondary uses of health data, models, and biological materials, including learning health systems, public health surveillance, quality improvement, operations, commercial product development, and artificial intelligence (AI)–enabled translational uses that rely on health data or material lineages. We introduce ethical governance continuity dissolution (EGCD), the progressive and sometimes irreversible loss of domain-specific governance authority across a data, model, or biological material lineage, such that no coherent set of actors, instruments, or community processes can authorize, constrain, monitor, adjudicate, or remediate current use in relation to the persons and communities of origin. EGCD is distinct from consent staleness, function creep, contextual integrity violation, algorithmic drift, or a single defective data use agreement. It is the systemic condition in which several such failures together compromise the authority needed to govern current use. Building on our prior work on representational veracity and the continuity trap, we propose a diagnostic architecture that assesses six governance authority domains, applies three diagnostic criteria, scores each domain from 0 to 3 within a Continuity Authority Matrix (CAM), and stages severity from 0 to 4. We also propose an Ethical Continuity Governance and Response Mechanism (ECGRM) comprising a continuity registry, the CAM, trigger-based review, a Data Lifecycle Governance Officer function, a Continuity Dissolution Review Board, corrective and preventive action, cross-institutional audit, and community-governance integration. We use a publicly reported Royal Free–DeepMind Streams example to show how domain scores can identify stage 2 or 3 risk without converting the framework into a retrospective legal judgment. Implementation is proportionate, so these functions may operate within existing structures, particularly in underresourced institutions. The score and staging thresholds are conceptual triage aids, not validated metrics or automated determinations of ethical permissibility, and they require empirical validation and interrater reliability testing. Their purpose is to prevent the silent loss of governance authority while data, models, and biological materials continue to affect the lives, identities, and community standing of the persons and groups from whom they were derived. Lawful public health surveillance under a competent authority is a legitimate governance handoff, not EGCD; EGCD arises when authority is not transferred, traceable, accountable, or remediable. This viewpoint is addressed to research ethics committees and institutional review boards, data access committees, data stewards, biobank and registry leaders, AI governance teams, regulators, funders, community governance bodies, and investigators.

Large language models (LLMs) are increasingly used as health information intermediaries. Whether they provide comparable accuracy and communication quality across languages has direct implications for health information equity; however, systematic bilingual evaluations remain limited.

Asthma and chronic obstructive pulmonary disease (COPD) affect more than 650 million people worldwide and remain leading causes of disability, with a rising burden as populations age. Conversational agents (CAs) may offer a more interactive alternative. However, the evidence in obstructive lung disease has not been mapped.

In a retrospective analysis of 34,449 adults using an unsubsidized, tirzepatide-supported digital weight loss service (DWLS) in Australia, patients entering via peer referral showed higher 6-month program adherence and greater percentage weight loss than propensity score–matched nonreferred patients, suggesting that peer referral pathways may support retention and effectiveness in medicated obesity care.

Cyberattacks against health care organizations are rising in frequency and shifting in nature from data theft to intentional denial of care.

Adults receiving long-term hemodialysis often experience a multidimensional symptom burden that affects their functional status and daily activities. Effective symptom management may help reduce this burden, preserve functional status, and improve the overall treatment experience. However, evidence remains limited for nonpharmacological interventions that are safe, acceptable, and easy to integrate into routine hemodialysis care. Virtual reality (VR) may be a promising approach for symptom relief and supportive management, but its overall effectiveness and safety in adults receiving hemodialysis remain unclear.

Gaps in pharmaceutical governance could widen with the adoption of AI, even as AI promises better pharmacovigilance in low-income countries (LICs). While advanced regulatory systems like Australia’s are integrating AI into pharmaceutical governance, LICs with underdeveloped regulatory capabilities, such as South Sudan, lag behind. The potential divergence disorients the World Health Organization’s “Medicine Without Harm” agenda and effective global pharmacovigilance. Moreover, evolving global governance initiatives, including the newly established United Nations scientific panel on AI, may be hampered by this global divergence in capabilities. This makes 3 critical interrelated questions: what are the moral trade-offs in the introduction of AI in health care, what power dynamics impact the introduction of AI into health systems, and how could AI be used for pharmacovigilance in LICs? This viewpoint aims at informing global policies and regulations on AI in pharmacovigilance. It uses clinical, policy, and regulatory practitioner insights to synthesize evidence on the ethical, economic, and clinical contours of AI in pharmacovigilance. It contrasts the high-income context of Australia with the low-income context of South Sudan and shows that national capabilities are instrumental for institutionalizing global practice. It identifies current ethical challenges with applying AI and digital health, which straddle epistemic, normative, and metaethical domains, such as misguidance, cultural devaluation, and trust deficit. These filter into demerits observed with current applications of AI to pharmacovigilance, from the detection of adverse drug events and adverse drug reactions to the simulation of clinical trials. The merits of current applications are multiple and depend on data quality, ranging from the detection of adverse drug reactions to real-time surveillance of medical errors and predictive application to population risk quantification of adverse drug events. The widening gaps in global capabilities amid rapid evolution of AI suggest the need for inclusive global governance in the early stages, especially because AI may be deterministic and effects may not be retrospectively surmountable. The viewpoint also assesses the sufficiency of current evaluation frameworks, noting that health economic models currently lag in capturing gains and losses from the adoption of AI in health systems, digital health frameworks are largely retrospective and overlook sociopolitical and financial contexts, and influential service-oriented frameworks for health systems overlook outcomes. It observes that, although AI could be harnessed across the breadth of the pharmaceutical system, effective evaluation of potential risks is hampered by upstream decisions in software development and procurement, which preclude aspects of subsequent application. This introduces inscrutability and weakens clinicians’ role in risk adjudication, which may worsen with nonrepresentative evolution of AI. Using these insights and a case study on the low-income context of South Sudan, the viewpoint commends an integrated health systems framework and country-level investments in infrastructure and regulatory capabilities as requisites for effective global governance and equitable use of AI in pharmacovigilance.