JMIR Medical Informatics

Severe tubular atrophy/interstitial fibrosis are critical pathological features associated with poor prognosis in IgA nephropathy (IgAN). Early identification of patients at high risk for severe tubular damage could guide clinical management and improve outcomes.

Patients with diabetes are at higher risk of developing liver cancer. Nevertheless, risk factors and their interaction patterns have rarely been compared between patients with and without diabetes, nor have their interactions been incorporated into scoring system development.

Medical ethics provides a moral framework for the practice of clinical medicine. Four principles, that is, beneficence, nonmaleficence, patient autonomy, and justice, form the cornerstones of medical ethics as it is practiced today. Of these 4 principles, patient autonomy holds a pivotal position and often takes precedence in ethical dilemmas that result from conflicts among the 4 principles. Its importance serves as a constant reminder to the clinician that the “needs of the patient come first.” With their remarkable ability to process natural language, large language models (LLMs) have recently pervaded nearly every aspect of human life, including medicine and medical ethics. Reliance on tools such as LLMs, however, poses fundamental questions in medical ethics, where human-like reasoning, emotional intelligence, and an understanding of local context and values are of utmost importance.

Diabetic Nephropathy (DN), a severe complication of diabetes, is characterized by proteinuria, hypertension, and progressive renal function decline, potentially leading to end-stage renal disease. The International Diabetes Federation projects that by 2045, 783 million people will have diabetes, with 30%-40% of them developing DN. Current diagnostic approaches lack sufficient sensitivity and specificity for early detection and diagnosis, underscoring the need for an accurate, interpretable predictive model to enable timely intervention, reduce cardiovascular risks, and optimize healthcare costs.

Early diagnosis of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (HBV) is challenging. Models that combine novel biomarkers with clinical features may improve both early diagnosis and risk stratification, but few have been systematically validated.

Kidney cancer remains a significant challenge in oncology, with accurate prognostic assessment being crucial for postoperative management. While radiomics has shown promise in cancer prognosis, there is limited research on comprehensive models that effectively integrate radiomics features with clinical parameters for kidney cancer survival prediction.

Health information exchange (HIE) supports clinical decision-making in emergency medicine settings. Despite evidence and policies that encourage the adoption of HIE, use by clinicians is limited. Moreover, few studies examine HIE use years after adoption by hospitals or clinics.

Clinical research studies rely on schedules of activities (SoAs) to define what data must be collected and when. Traditionally presented in tabular form within study protocols, SoAs are critical for ensuring data quality, regulatory compliance, and correct study execution. Recent efforts, such as the Health Level 7 Vulcan SoA Implementation Guide, have introduced Fast Healthcare Interoperability Resources (FHIR) as a standard for representing SoAs digitally. However, current approaches primarily handle simple schedules and do not adequately capture complex requirements such as conditional branching, repeat cycles, or unscheduled events—features essential for many study designs, particularly in oncology.

Liver fibrosis is a pathological outcome of chronic liver injury and a hallmark of multiple chronic liver diseases. Magnetic resonance elastography (MRE) provides a non-invasive modality for evaluating the severity of liver fibrosis.

Accurate staging of esophageal cancer is crucial for determining prognosis and guiding treatment strategies, but manual interpretation of radiology reports by clinicians is prone to variability and limited accuracy, resulting in reduced staging accuracy. Recent advances in large language models (LLMs) have shown promise in medical applications, but their utility in esophageal cancer staging remains underexplored.

The burden of paralytic ileus (PI) in the intensive care unit (ICU) remains high, and the Charlson Comorbidity Index (CCI) is strongly associated with the prognosis of several acute and chronic diseases. However, evidence specifically evaluating the prognostic value of CCI in ICU patients with PI remains limited.

Clinical natural language processing (cNLP) techniques are commonly developed and used to extract information from clinical notes to facilitate clinical decision making and research. However, they are less established for rare diseases such as lymphoid malignancies due to the lack of annotated data as well as the heterogeneity and complexity of how clinical information is documented. In addition, there is increasing evidence that cNLP techniques may be prone to biases embedded in clinical documentation or model development. These biases can result in disparities in performance when extracting clinical information or predicting patient outcomes.