Journal of Medical Internet Research

Integration of large language models (LLMs) into health care has accelerated rapidly, yet reliability concerns pose potential risks to patient safety. Although human evaluation has been widely used as an important approach for assessing LLM reliability, a systematic understanding of how such evaluations have been operationalized across studies remains limited.

The use of data to support decision-making and primary processes is central to establishing data-informed care. Yet, data remain underutilized for quality improvement in long-term care (LTC). Data maturity reflects an organization’s capability to use data, for example, to guide strategic objectives.

The Registry of Stroke Care Quality (RES-Q) is a health care quality improvement platform used globally. RES-Q collects structured quality-of-care data for patients with stroke, requiring clinicians to manually extract information from electronic health records or documents such as discharge summaries. This process is essential but time-consuming, particularly given the variability, length, and semistructured nature of clinical reports.

Secondary use is now the ordinary condition of data science health research rather than an exception to it. Electronic health records collected for clinical care become prediction tools and inputs for generative AI; imaging archives become foundation-model corpora; genomic datasets become resources for polygenic risk scores; and legacy biospecimens become renewable, indefinitely distributable cell lines. Governance has responded by emphasizing verifiable instruments such as provenance logs, repository approvals, broad-consent forms, data-use agreements, model cards, records of processing, and locality-preserving architectures. These instruments are necessary, and they answer real questions about lineage, privacy, institutional responsibility, and accountability, but they are not sufficient to establish that a present use remains ethically justified. We define ethical continuity as the persistence of normatively relevant relationships between the original conditions of data generation or material collection and subsequent downstream uses, such that current uses remain justifiable in light of the expectations, permissions, meanings, and relational obligations present at entrustment. We then define the Continuity Trap as a review-stage governance error in which a salient signal of continuity in one domain is treated as sufficient evidence of ethical continuity overall, causing inquiry into the remaining domains to close prematurely. The trap is not ordinary noncompliance, ethics creep, or a demand for universal rereview; it is a cross-domain inference error that can arise even in careful, good-faith review. We distinguish it from proxy closure, of which it is a continuity-specific subtype, and from Goodhart’s and Campbell’s laws, which describe how measures degrade once they become targets. We operationalize ethical continuity across 4 domains: provenance, semantics, authorization, and relational standing, developed in our Representational Veracity framework, and we show that these domains can diverge as data are linked, transformed, modeled, and redeployed. We identify the institutional mechanisms—provenance privilege, descriptor sedimentation, authorization fossilization, and community effacement—that cause auditable signals to be overread, and we examine how the US Health Insurance Portability and Accountability Act (HIPAA) of 1996, the General Data Protection Regulation, the European Health Data Space, US Food and Drug Administration guidance, the US National Institute of Standards and Technology (NIST) AI Risk Management Framework, and federated-learning governance can reduce risk while still inducing continuity traps. We apply the framework to consent and nonconsent settings, including public health, immunization, syndromic, and wastewater surveillance, polygenic risk scores, induced pluripotent stem cells, federated learning, and health-related large language models. The policy implication is trigger-based continuity review: rather than rereviewing every reuse, investigators and reviewers should identify the weakest continuity domain at the present data stage and impose a domain-matched safeguard, recorded in a short continuity statement. This reframing is intended for the committees, repositories, funders, and governance bodies that decide whether reuse may proceed, and it matters most in cross-border and low-resource settings. Provenance should begin ethical review; it should not end it.

Cognitive decline in older adults imposes a major global burden, with physical inactivity a leading modifiable risk factor for dementia. Digital physical exercise interventions offer scalable alternatives to traditional programs, but comparative effectiveness across cognitive domains remains unclear.

Digital health technologies, including telemedicine, electronic medical records, digital health tools (DHTs), and AI, are transforming European primary care, but evidence on how general practitioners (GPs) experience these tools remains fragmented.

Community-based management of exacerbations in high-risk patients with chronic obstructive respiratory diagnoses remains a major challenge. Hybrid care interventions, combining digital support with in-person, patient-centered care, have shown efficacy to reduce unplanned hospitalizations in controlled trials. However, an efficacy-effectiveness gap remains, indicating the complexities of its deployment and sustainable adoption in real-world scenarios.

AI-based models for predicting mortality have shown potential for intensive care unit (ICU) patients, but evidence regarding their cost-effectiveness remains limited.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children and adolescents. Digital therapeutics (DTx) show promise as nonpharmacological interventions, but the comparative efficacy of different DTx modalities remains unclear.

Automated multimedia analysis of remotely recorded tasks offers a scalable approach to screening and remote monitoring of movement disorders such as Parkinson disease (PD). However, unsupervised recordings often suffer from quality issues that compromise model reliability. General multimedia quality checks may not detect task-specific failures, such as poor hand visibility during finger-tapping, inadequate facial framing during smile tasks, or background noise during speech tasks.

Patients increasingly use generative AI to interpret symptoms and seek health information, yet limited evidence shows how AI-assisted self-diagnosis is integrated into care-seeking and related to clinical interactions and patient–physician relationships.

AI has shown significant potential in intensive care unit (ICU) nursing practice, enhancing efficiency, decision-making, and patient safety. However, evidence regarding the implementation factors of AI in ICU nursing remains limited, particularly from the perspective of nursing leadership.