JMIR Medical Informatics

Major adverse renal and cardiovascular events (MARCE) have been proposed as a comprehensive end point reflecting both cardiac and renal outcomes in patients with acute myocardial infarction (AMI) and chronic kidney disease (CKD). However, existing prediction tools lack accuracy and generalizability for this high-risk population.

In a cross-sectional analysis of traditional Medicare Fee-For-Service beneficiaries aged 66 years or older with outpatient oncology-related encounters, we found that telehealth modestly extended patient-provider geographic reach but largely operated within existing office-based catchments and preserved rural-urban distance gradients. Equitable telehealth policy should target patients with high travel burden while supporting digital access, navigation, and cross-jurisdiction continuity.

Cough variant asthma (CVA) is a prevalent but underdiagnosed subtype of chronic cough, largely due to the limited accessibility of pulmonary function tests in primary care.

Stress and inflammatory bowel disease (IBD) symptoms may vary over short time scales, but these dynamics are not well captured by intermittent clinical assessments.

Negation in radiology reports (eg, “no evidence of dissection”) is essential for accurately conveying diagnostic information. Clinical natural language processing systems, however, often fail to determine whether findings occur within negated or speculative contexts, leading to excessive false keyword alerts, noise in information extraction, and reduced precision in downstream clinical or research applications. Transformer models have advanced detection capabilities but require costly expert labeling. Large language models (LLMs) may provide label-efficient alternatives through few-shot prompting or knowledge distillation.

Electronic health records (EHRs) are central to patient-centered care. However, many current EHR systems do not represent, exchange, or reuse genomic findings in structured, interoperable, and clinically actionable forms. Instead, genomic results are often retained in static reports. This limits their use in routine clinical care and clinical decision support. It also restricts their future reinterpretation as scientific knowledge evolves, as well as their reuse for research within appropriate governance frameworks. To support precision and personalized medicine, we advocate that clinically relevant genomic findings should be represented within EHRs as structured, phenotype-linked, interoperable, and versioned data. Raw and intermediate genomic files, however, should remain in appropriately governed laboratory or genomics repositories. This viewpoint is intended for health policymakers, health informatics leaders, EHR developers, research and clinical genomics laboratories, and clinicians involved in implementing genomics into routine care. It is informed by selected published literature and relevant technical standards. It is also informed by operational experience at the Cyprus Institute of Neurology and Genetics and multidisciplinary discussions from the Gen2EHR Implementation Guidelines Workshop conducted within the Extended EHR@EU Data Space and European Health Data Space for Secondary Use @CY initiatives. We examine technical, organizational, clinical, ethical, legal, and governance challenges, including inconsistent reporting formats and unclear clinical responsibility. We propose a staged, workshop-informed implementation pathway for evaluation within the European Health Data Space. The pathway combines Health Level 7 Fast Health Interoperability Resources Genomics Reporting with complementary standards and terminologies. It also includes transitional dual PDF and structured Fast Health Interoperability Resources reporting, governance for access, version control, reinterpretation, and clinical responsibility, as well as multidisciplinary pilot validation in real diagnostic workflows. The clinical effectiveness and transferability of the proposed pathway across European health systems remain to be established.

Cross-domain chest radiograph report generation may produce formally valid reports that are nevertheless repetitive, weakly case-specific, or missing clinically relevant findings. These failures can be missed when evaluation relies on a single language-overlap or clinical-label score.

Therapeutic drug monitoring (TDM) aims to optimize clinical outcomes but is hindered by logistical delays and susceptibility to data errors. Integrating model-informed precision dosing with point-of-care testing, fortified by rule-based validation and alerts, forms a transformative clinical decision support system (CDSS) essential for securing this complex workflow.

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Health AI investment is accelerating across the Gulf Cooperation Council (GCC), yet the region’s health AI research output has never been systematically quantified. National research capacity is a structural precondition for the safe local development and governance of clinical AI, but no scientometric baseline exists for the 6 GCC states.

The development of structured optical character recognition (OCR) systems for Korean medical documents is hindered by strict privacy regulations that limit access to real clinical data. Existing synthetic document frameworks are primarily designed for generic layouts and Western languages, and fail to capture the complex formatting, bilingual terminology, and hierarchical field semantics required for clinical OCR and document-understanding tasks in hospital environments.