Journal of Medical Internet Research

Medical AI is often evaluated using aggregate measures of discrimination, calibration, and accuracy. However, these measures can obscure clinically important variation across patient groups, institutions, devices, and workflows. This viewpoint defines refined exclusion as a governance condition in which an AI system appears successful in aggregate, while uncertainty, error, or reduced clinical reliability is concentrated in populations that are insufficiently represented, measured, validated, or monitored. The concept does not replace algorithmic fairness, hidden stratification, dataset shift, or subgroup performance analysis. It connects these mechanisms to a distinct consequence: an unequal distribution of safety that remains inadequately detected or corrected. Drawing on purposively selected, illustrative evidence from population health management, chest radiography, dermatology, computational pathology, medical foundation models, and clinical measurement, we distinguish model-level disparity, patient safety signals, and documented patient harm. We then frame data justice as a complementary governance approach with distributional, procedural, and substantive dimensions. The proposed lifecycle decision gates address intended use, subgroup learnability, data provenance, validation, procurement, local deployment, monitoring, updates, and patient feedback. Each gate links minimum evidence to decision authority and 1 of 4 actions: proceed, enrich or validate, restrict use, or pause or retire. Governance intensity should be proportionate to clinical risk and evidentiary uncertainty. By linking subgroup evidence gaps to institutional decisions and corrective action, the framework shifts attention from whether a model performs well on average to whether its safety is demonstrable for the populations and settings in which it will be used.

The rapid advancement of AI, particularly generative AI (GenAI), is reshaping educational paradigms. However, its utility in preclinical digital medical skills education remains insufficiently explored.

Real-time audiovisual connections between health care providers (HCPs) in neonatal care, known as TeleNeonatology (TeleNeo), can improve neonatal care. For patients and families, TeleNeo was found to improve patient outcomes and facilitate family-integrated and patient-centered care by ensuring timely access to expert involvement regardless of location, which can strengthen trust and reassurance. For clinicians and health care organizations, TeleNeo enables expert decision-making, fosters continuous professional development, promotes knowledge exchange between hospitals, and increases staff confidence in managing complex medical cases. However, organizational, technical, and infrastructural requirements can hinder successful implementation and sustained adoption of technological interventions, such as TeleNeo. Implementation can be time-consuming and may fail due to the challenges encountered during the implementation process, particularly when incorporating the intervention into existing workflows across multiple hospitals. Nevertheless, there are case studies that demonstrate successful implementation of TeleNeo into routine care. Informed by international experience and theoretical underpinnings of implementation science, this tutorial presents a toolkit to provide step-by-step guidance for health care institutions considering TeleNeo implementation. The objective of the toolkit is to help health care organizations effectively and efficiently implement TeleNeo in their neonatal care pathways. The toolkit provides a structured guide that encompasses the entire implementation process, from initial ideas to stakeholder engagement, workflow design, and program evaluation. It includes checklists, planning guidelines, and tools to help teams design a customized implementation strategy for their institution.

Healthy lifestyle behaviors, including a balanced diet, regular physical activity, adequate sleep, avoiding tobacco, moderating alcohol consumption, and stress management, are associated with reduced risk of chronic disease and improved well-being. In recent years, digital interventions have emerged as cost-effective platforms for promoting these behaviors, yet few have been integrated into services delivered by certified exercise practitioners (CEPs) or focused on multiple domains of health.

Surgical site infections (SSIs) remain a major cause of health care–associated infections, and early prediction is essential for improving patient outcomes. Machine learning (ML) has shown potential for SSI prediction; however, clinical implementation requires models that are both accurate and explainable. Despite recent progress in explainable ML, its clinical application to SSI prediction remains limited.

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Implementing digital health technologies is challenging because implementation is shaped by interacting technical, organizational, professional, and contextual factors. Although implementation frameworks such as the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework support understanding this complexity, translating them into practical tools for routine implementation remains difficult.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are heterogeneous clinical states that may represent early or at-risk stages of Alzheimer disease (AD) and other dementias in some individuals. Improved characterization and risk stratification in these populations may facilitate timely evaluation and intervention. Electroencephalography (EEG), a noninvasive, cost-effective neurophysiological technique with high temporal resolution, holds significant potential for elucidating neural mechanisms and providing candidate neurophysiological markers associated with SCD and MCI.

As populations age globally, AI-enabled digital health interventions (DHIs) are increasingly being adopted to support integrated, person-centered care for older adults. However, despite rapid advances in AI technologies, their economic value in older care remains poorly understood.

There is tremendous enthusiasm for the use of AI in health care because of the ability to analyze existing data for preventative, diagnostic, and treatment support. Agentic AI can feasibly provide access to large real-world datasets for the generation of real-world evidence for health care and clinical applications to health care providers, researchers, and administrators without access to large analytic programming resources.

Nonpharmaceutical interventions (NPIs), including digital contact tracing (DCT), are central to control the spread of airborne pathogens. Nevertheless, the effectiveness of individual interventions and the role of personal behavior remain insufficiently understood.

Temporal relation extraction (TRE) in clinical narratives is crucial for understanding patient history, disease progression, and treatment pathways. However, it remains challenging due to limited annotated data and to the complexity of clinical text, including domain-specific terminology, inconsistent information, and implicit temporal reasoning. These challenges are particularly acute for rare diseases, where information such as the date of diagnosis or the onset of key phenotypes is rarely available in structured data, despite being essential for estimating diagnostic delay and reconstructing the natural history of the disease.