JMIR Medical Informatics

Clinical data warehouses store large volumes of unstructured text containing valuable information about patients’ medical status. Traditional extraction systems based on named entity recognition (NER) identify medical terms but often fail to capture the contextual cues needed for accurate interpretation. Existing approaches to context-aware extraction differ in their reliance on expert annotation, computational power, and lexical resources, leading to uneven feasibility across institutions. Combined with heterogeneity in documentation practices and data-sharing restrictions, these limitations hinder the scalability and reuse of trained models. There is thus a need for practical frameworks that can be deployed and adapted locally within medical institutions.

Kidney transplant recipients require lifelong self-management and follow-up care to maintain allograft function. Mobile health (mHealth) effectively improves self-management behaviors and clinical indicators, consequently enhancing nursing care quality. However, these apps commonly face challenges, including low adoption rates and high discontinuation. Although researchers have explored associated facilitators and barriers from various perspectives, a systematic review of these influencing factors is lacking.

Medical ambient artificial intelligence (AI) scribes reduce documentation burden, but the current evidence is almost entirely from English systems. In the Arabic-speaking world, physicians converse mainly in Arabic and write clinical notes in English, adding cognitive burden. Due to scarce corpora in the Arabic language, the development of Arabic-enabled AI speech technologies has been challenging. Here, we address this gap by developing and evaluating a bilingual Arabic-English medical AI scribe.

Digital health literacy (DHL) is the ability to locate, understand, evaluate, and apply health information in digital environments. It is essential for older adults to effectively engage with contemporary health care. However, existing DHL assessments primarily rely on self-reported measures, which are susceptible to subjective bias and often fail to capture actual performance. There is a need for a comprehensive, data-driven approach that integrates objective performance indicators with self-assessments to accurately predict and explain DHL levels in older adults.

Hyperhomocysteinemia (HHcy) is recognized as an independent risk factor for coronary heart disease (CHD), yet accurately predicting CHD risk in patients with HHcy remains a challenge. This study aimed to develop and validate multiple machine learning models for predicting CHD risk in patients with HHcy and elucidate key predictors using Shapley Additive Explanation (SHAP) algorithms.

Dynamic Personalized Optimization (DPO) is introduced as a conceptual framework that defines core artificial intelligence (AI) functions required to deliver real-time, personalized, and optimized treatment in digital therapeutics (DTx). DPO continuously refines therapeutic strategies by integrating patient data, treatment content, usage feedback, and status measurements to provide real-time, personalized treatment. Using predictive AI models, DPO adapts treatment approaches based on patient responses, thereby improving therapeutic effectiveness. Furthermore, this paper explores the potential role of large language models (LLMs) in supporting DPO by processing diverse and complex data formats. By addressing current limitations in real-time personalization within DTx, DPO establishes a structured, AI-driven approach to delivering tailored digital interventions. This framework ultimately aims to enhance treatment efficacy and improve patient engagement.

Artificial intelligence–based medical devices (AIMDs) have emerged as transformative technologies in modern health care. However, comprehensive analysis of recent approval trends and characteristics of AIMDs in China remains limited.

Health care public reporting (PR) refers to making information about the quality and performance of health care providers available to the public. The primary targeted use of PR is the selection of a health care provider. Previous studies suggest that PR has improved health care quality; however, the overall adoption rate of PR systems remains low. Misalignment between PR information and users’ actual needs can explain this gap.

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Autosomal dominant polycystic kidney disease (ADPKD), characterized by progressive cyst growth and renal decline, is the leading genetic cause of end‐stage renal disease.

Telepathology is widely recognized for improving diagnostic access in remote areas and is strongly promoted in China to address the shortage of pathologists. However, a comprehensive national assessment of its implementation and multistakeholder perspectives remains lacking.

Observational data are fundamental to medical research but present formidable challenges for causal inference. Machine learning–based causal discovery algorithms have emerged as a promising solution to identify causal structures directly from such data. However, the current literature is skewed toward theoretical and methodological innovations, with a critical gap in systematic assessments of performance in medical research settings and a lack of practical guidance for clinicians and researchers on selecting and applying these algorithms in specific medical contexts.