Journal of Medical Internet Research

Dementia is a growing health burden worldwide; however, scalable, low-cost strategies for early risk identification are lacking. Because cardiac and cognitive aging are closely associated, a routinely collected 12-lead electrocardiogram (ECG), interpreted using foundation model embeddings, is a promising and accessible candidate marker. However, it is unclear whether such embeddings carry predictive information independent of chronological age—the dominant risk factor for dementia—and whether the underlying changes follow a detectable prediagnostic trajectory.

The ethics of autonomous clinical AI are controversial. Traditional ethics assessments have focused on AI model performance rather than health system outcomes. Nichol and colleagues translate established research ethics principles into 9 practical recommendations for trials of autonomous clinical AI. Their findings support an evolution in clinical trial design. The accuracy and performance of the model is important, but the ethical unit of evaluation should be the complete care pathway, with prespecified obligations for subgroup performance, follow-up care, system failures, and model change.

A central concern in ecological momentary assessment (EMA) research is whether repeated self-report alters the reporting process itself. In most EMA domains, longitudinal stability of reporting is assumed but rarely tested.

Gestational diabetes mellitus (GDM) has a rising global prevalence and brings short-term and long-term adverse risks for mothers and fetuses. Conventional in-person GDM care is limited by time and geographic barriers, often resulting in suboptimal compliance and delayed individualized interventions. Telemedicine is increasingly applied in GDM management; however, randomized controlled trial (RCT) evidence remains inconsistent.

Over the last 2 decades, a large global marketplace has emerged in which businesses use direct-to-consumer advertising to sell putative stem cell treatments that are unsupported by convincing scientific evidence of safety and efficacy. It is common for such purported stem cell therapies not to have been reviewed and approved by national regulatory authorities, even though they are supposed to undergo rigorous premarketing review by regulatory bodies responsible for evaluating such medical products and determining whether they are backed by sufficient evidence to warrant approval. The marketing claims, commercial activities, and clinical practices of such businesses have resulted in patients experiencing serious harms, including loss of life, physical injuries, financial losses, and avoidable delays in accessing evidence-based treatments. Such businesses are also responsible for collective harms, including diverting patients from participating in legitimate stem cell clinical studies, damaging the field of credible stem cell research, and contributing to public confusion and misunderstandings about how to distinguish evidence-based stem cell therapies from stem cell interventions that are not backed by robust data. Researchers in bioethics, health law, the social sciences, and other academic fields have made important contributions to the critical analysis of direct-to-consumer digital marketing claims made by businesses selling unproven and unapproved stem cell interventions. Scholars studying such marketing representations have documented the use of persuasive “tokens of legitimacy” to make advertised stem cell interventions seem credible, trustworthy, and backed by current scientific evidence. Acknowledging the value of such scholarship, there is a growing need for the development of best practices in research methods and data-archiving practices. In particular, there is a need for increased digital archiving of online marketing claims made by businesses selling supposed stem cell treatments. Numerous parties can benefit from increased digital archiving of such problematic marketing representations. However, analyzing, archiving, and critiquing the marketing claims, business operations, and clinical practices of such businesses must be done in a careful, rigorous, and evidence-based manner that is attentive to the intimidating tactics some of these businesses use when responding to public criticism. Researchers need to document the marketing representations made by businesses selling unapproved and unproven stem cell interventions in ways that minimize exposure to legal threats and other efforts to suppress critical analysis. Using the Wayback Machine or other comparable archiving tools to create digital repositories of such marketing representations is one way to mitigate such threats, by capturing the exact claims such businesses make when promoting putative stem cell therapies. Archiving such digital marketing representations is important for academic research and can play a valuable role in other contexts, such as in legal actions taken against businesses using purportedly deceptive marketing claims to sell unproven and unapproved stem cell-based interventions.

Human trials in dietary and cosmetic domains generate large volumes of multimodal data but remain limited in their ability to produce integrated and actionable insights. Data such as intake records, physiological biomarkers, imaging outputs, and user-reported outcomes are typically collected through disconnected systems and analyzed independently, restricting the ability to establish time-resolved relationships between exposure and response. In this viewpoint, we define this limitation as the AI transformation (AX) gap, representing a structural disconnect between data generation and actionable knowledge in human trials. Drawing on evidence from real-world industry demand, we highlight how this gap is consistently observed across dietary intervention trials, cosmetic human trials, and underlying data systems. We propose a conceptual AX-driven framework that integrates multimodal data acquisition, continuously aligns intervention exposure with corresponding physiological, phenotypic, and participant-reported responses across time, and supports centralized analytical processing. The framework is intended to provide a unified perspective for transforming fragmented human-trial data into integrated, interpretable, and actionable knowledge. Although the proposed framework has not yet been empirically validated, it offers a conceptual foundation for future development and evaluation of integrated data platforms in dietary intervention and cosmetic human trials. Closing the AX gap may facilitate the transition from fragmented data collection toward human trials in which multimodal exposure and response data are continuously linked across time to support more integrated and individualized interpretation in research and product development.

Type 2 diabetes mellitus (T2DM) is a chronic condition that requires ongoing self-management. While chatbots offer scalable monitoring and management, evidence on their integration into structured hospital tele-support workflows, where patient engagement and digital readiness may differ from those in standalone digital interventions, remains limited.

In recent years, remote exercise services (“telerehabilitation”) have been increasingly explored and implemented, including in supportive cancer care, on the promise of greater convenience, access independent of geographic location, reduced infection risk, and cost savings. Ethical commentary on telehealth in general is substantial, but sustained discussion of questions specific to telerehabilitation remains comparatively sparse. As contributors to PREFERBALE-II, a European Union–funded project evaluating live-remote exercise for people recovering from cancer treatment, we sought practical guidance on how to deliver such services responsibly, and found little to work with. This prompted this viewpoint, which sets out our own position as researchers and clinicians engaged in this work. We draw on a purposive sample of professional guidance documents, which were assembled through our professional networks and a supplementary web search, and on a narrative reading of qualitative studies chosen to represent accounts of benefits as well as burdens. We read these documents through an analytical lens of responsible innovation. We make 3 arguments. First, the ethical content of current telerehabilitation guidance is largely confined to the products and processes of innovation, covering safety, privacy, consent, and compliance, while the question of purpose, that is, whether, for whom, and why remote rehabilitation should be offered at all, is left largely unasked. Anticipation appears only implicitly, and reflexivity, inclusion, and responsiveness barely at all. Responsibility is repeatedly delegated to individual professionals without commensurate structural support. Second, the situated experience of users and professionals forms part of the ethics of telerehabilitation and is not merely data to be assessed. Reported benefits and burdens depend on social context, including age, digital literacy, connectivity, caregiver availability, language, and sensory or cognitive impairment, so an equity claim made for the modality in general may not hold for the individual patient. Third, the ethics of telerehabilitation consequently has to be institutionalized across 3 overlapping domains: the interpersonal and experiential quality of remote care, the legal and institutional conditions of delivery, and the positioning of telerehabilitation within health systems. We address these arguments primarily to rehabilitation professionals and the associations and standard-setting bodies that issue guidance for them, and secondarily to digital health and ethical, legal, and social implications researchers, policymakers, and health-system decision-makers who commission or implement such services. We offer an agenda for a broader ethics conversation across professional societies, curricula, and affected publics, together with observations from a consortium put forward as hypotheses for study.

The COVID-19 pandemic accelerated the adoption of virtual care, including patient-to-provider asynchronous secure messaging (ASM). However, while ASM offers access and convenience, the surge in message volume can increase physician workload and contribute to burnout. Furthermore, it has raised additional concerns about message appropriateness, medicolegal liability, and sustainability of this communication virtual care modality.

Large language models (LLMs) are increasingly used to generate and evaluate patient-facing medical materials. Through persona prompting, LLMs can be instructed to simulate responses from individuals with different demographic and experiential backgrounds, offering a scalable approach for approximating patient feedback. However, it remains unclear how closely these simulated persona-based responses reflect real patient perspectives, and whether this alignment is consistent across demographic subgroups.