Journal of Medical Internet Research

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.

For most with Parkinson disease, a movement disorder, voice and speech are impaired at some point, which presents an opportunity to use digital recordings and sophisticated machine learning to assist objective clinical characterization of the condition. However, as highlighted by Shukla et al in their August 20, 2026 paper, ad-hoc observational datasets typically contain spurious causal associations that escape a purely statistical analysis. This commentary argues that causal inference, and ultimately, diagnostic clinical trials, are required to establish a direct mechanistic relationship between disease and algorithm predictions.

The traditional method for diagnosing obstructive sleep apnea (OSA) through polysomnography may be expensive and inaccessible. Recent developments in AI propose the use of photoplethysmography (PPG) to aid OSA diagnosis.

Large language models (LLMs) have the potential to improve the efficiency of evidence synthesis, but their reliability in performing complex tasks such as risk-of-bias (ROB) assessment in randomized controlled trials (RCTs) remains unclear.

Digital health interventions (DHIs) offer scalable ways to reach youth. While DHI research often poses minimal risk, guardian permission is typically required for minors to participate. As a result, requiring guardian permission may unintentionally obstruct safe, high-quality research and delay its translation.

Pediatric health care requires distinct considerations, including caregiver involvement and developmental differences in cognition and communication as children gain autonomy, particularly as pediatric health care chatbots gradually emerge. Because childhood and adolescence are formative periods for health behaviors and self-management practices, pediatric chatbots also warrant evaluation against long-term rather than immediate outcomes.

Digital health technologies increasingly support long-term stroke management and rehabilitation, but technophobia may hinder their acceptance and sustained use among older patients with stroke. Evidence in this population remains limited.

Real-world gait assessment has gained momentum in populations with walking impairments, offering insights beyond standardized tests and supporting the integration of remote monitoring into clinical care. However, its potential remains limited by the lack of validated population- and context-specific digital biomarkers.

Emergency triage of anterior circulation large vessel occlusion (LVO) is constrained by delays in vascular imaging, specialist interpretation, and transfer decision-making. Noncontrast computed tomography (NCCT) is often obtained first in suspected stroke, but visual recognition of LVO in NCCT images is difficult outside specialist settings. NCCT-based AI may provide an early human-in-the-loop escalation signal.