JMIR Medical Informatics

Type 2 diabetes mellitus (T2DM) combined with hypertension significantly increases mortality risk, yet accurate risk prediction models remain limited.

Foot orthosis prescription is a complex clinical decision-making process that involves selecting and combining multiple structural, functional, and material components based on heterogeneous biomechanical information. In routine outpatient practice, detailed biomechanical assessments are often incomplete, creating substantial variability in prescription decisions, and limiting the applicability of conventional machine learning (ML) models that assume fixed feature availability.

Natural language processing can extract meaningful information from clinical notes. However, human annotation is time-consuming and costly, and scarce data poses a challenge.

Sepsis-induced coagulopathy (SIC) is a common and severe complication in patients with sepsis, characterized by microvascular thrombosis, systemic endothelial damage, and markedly increased short-term mortality. Existing traditional clinical risk scoring systems demonstrate limited accuracy and fail to capture complex, nonlinear physiological interactions, underscoring the urgent need for advanced prognostic tools.

Mandatory notifications of infectious diseases in Germany were paper based until the implementation of DEMIS (Deutsches Elektronisches Melde- und Informationssystem für den Infektionsschutz; German Electronic Reporting and Information System) in 2020. DEMIS provides technical infrastructure for electronic reporting of infectious diseases to public health authorities, with the goal of streamlining processes through automation and reducing the burden of manual data entry.

Generative AI (GenAI) is increasingly entering health care through documentation support, communication tools, educational content generation, and other knowledge-intensive functions. However, organizational adoption remains uneven, and concerns related to privacy, security, output reliability, governance, workflow fit, and infrastructure continue to limit broader implementation. Although the literature increasingly discusses the skills health care workers may need in the GenAI era, less is known about how hospital managers view the organizational readiness required before such skills can be expected across the workforce. Hospital human resource (HR) managers are especially important in this regard because they are involved in training, competency development, workforce planning, and organizational change.

Delirium is a common and clinically important form of acute in-hospital mental status deterioration. Electronic health record (EHR)–based prediction models may support early identification and targeted prevention, but their methodological quality, validation rigor, and clinical readiness remain uncertain.

The rising global incidence of renal tumors necessitates precise diagnostic interventions. Accurate segmentation of computed tomography (CT) scans is essential for nephron-sparing surgery and radiotherapy. However, conventional manual delineation is labor-intensive and prone to significant interobserver variability due to tumor morphological heterogeneity. There is an urgent clinical demand for robust, automated segmentation solutions.

Large-scale international real-world evidence generation benefits from terminology harmonization. Despite widespread adoption of standardized vocabularies, their effective use and long-term sustainability at scale remain poorly understood.

Pharmacists’ prescription review is key to medication safety, but rising numbers of outpatient prescriptions and expanding formularies leave less time per case, increasing error risk. Large language models (LLMs) show promise, yet two barriers hinder routine use: first, most systems rely on cloud-based commercial models, risking data breaches by transmitting protected patient information externally, and second, retrieval-augmented generation (RAG) pipelines used to reduce hallucinations depend on complex text vectorization and vector databases, which hospitals with limited IT resources struggle to build and maintain.

Neonatal sepsis remains a major cause of neonatal morbidity and mortality in low- and middle-income countries (LMICs). Early diagnosis is challenging because of nonspecific clinical manifestations and delays in laboratory confirmation. Machine learning (ML) approaches using structured electronic health record (EHR) data may improve early risk stratification in neonatal intensive care units (NICUs).

Federated learning (FL) enables multi-institutional model training on clinical text without sharing raw data; however, gradient inversion methods can reconstruct sensitive information from shared model updates. The extent of such privacy leakage in FL applied to radiology reports, and the role of tokenizer design, remains unclear.