Journal of Medical Internet Research

Large language models (LLMs) are increasingly applied to causal explanation in public health and epidemiology. However, recent studies suggest that LLMs exhibit instability in processing temporal information, which raises concerns about their suitability for generating coherent causal explanations. Despite this, little is known about how distinct components of temporal information influence LLM-generated causal explanations in epidemiological contexts.

The accurate detection of clinical symptoms in electronic health records (EHRs) is pivotal for advancing large-scale genetic and longitudinal studies in psychiatry. Free-text clinical notes are an essential source of symptom-level information, particularly in psychiatry; however, the automated extraction of symptoms from clinical text remains challenging.

Screen use is almost universal, and numerous studies have highlighted both the benefits and the risks of this digital integration. However, evidence on the association between screen time and health outcomes among university students remains inconsistent. Substantial methodological variation has limited the synthesis and interpretation of existing findings.

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.