JMIR Medical Informatics

Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical translation face challenges.

Deep learning, particularly encoder-only transformer architectures, has demonstrated excellent performance in biomedical literature classification, facilitating evidence-based medicine, and knowledge synthesis. However, the opacity of these models’ decision-making processes limits their clinical interpretability, trustworthiness, and widespread adoption. Traditional explainable artificial intelligence methods, such as Shapley Additive Explanations (SHAP) and integrated gradients (IG), address this issue but often incur substantial computational overhead for text classification. Generative large language models may offer a novel approach to generating interpretable, context-aware explanations as autonomous agents.

Non–small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic prediction clinically essential. Machine learning models are increasingly used to assess prognosis; however, developing systems that combine high discrimination with clear, clinically interpretable reasoning remains challenging.

National Medical Products Administration of China has actively encouraged organizations to adopt the Clinical Data Interchange Standards Consortium (CDISC) for clinical data submission since 2020.

Chronic kidney disease (CKD) is a global health burden characterized by heterogeneous progression trajectories. Without timely and appropriate management, CKD can lead to increased morbidity and mortality and a reduced quality of life. Therefore, early identification of patients at high risk of developing end-stage renal disease (ESRD) or mortality is essential to facilitate timely intervention and improve patient outcomes.

Myocardial ischemia-reperfusion injury (MIRI) poses diagnostic challenges due to complex histopathological changes.

Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and remdesivir, do not considerably improve immune function, especially in high-risk individuals. SARS-CoV-2–specific T cells (CoV-2-STs) from vaccinated or convalescent donors are a promising new treatment that can enhance clinical outcomes and viral-specific immunity. CoV-2-STs improve T cell proliferation and recovery without raising safety concerns, according to randomized studies. Targeting patients for immunotherapy is made more difficult by the variability in COVID-19 progression brought on by variables like age and comorbidities. In order to further enable precision medicine and patient care, machine learning techniques are being used to analyze clinical data, predict disease severity, and optimize treatment. However, their use in guiding the treatment of novel therapies like CoV-2-STs using early cellular immunology data is limited and requires improvement.

Large language model (LLM) proofreaders for radiology reports generate many false positives (FPs) due to the low prevalence of errors.

Gastrointestinal (GI) cancers are a significant health concern in South Korea. Recently, machine learning (ML) models have emerged as powerful tools to support early screening efforts and identify people at risk before disease onset. However, the low incidence of GI malignancies in prospective cohorts leads to severe class imbalance, often causing ML models to favor the majority “healthy” class at the expense of clinical sensitivity.

Clinical note documentation is a vital yet time-intensive task in health care. While advancements in natural language processing have transformed many domains, generating accurate summaries of doctor-patient conversations remains underexplored due to the limited availability of open-source datasets. Large language models (LLMs), with their training on vast datasets, present a promising solution to this challenge.

Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the modified NUTRIC (mNUTRIC) score often rely on subjective assessments or delayed data, limiting their utility for early intervention in the dynamic intensive care unit (ICU) environment. Real-time, data-driven approaches using electronic health records offer a promising solution for automated and objective risk stratification.

Severe trauma remains a leading cause of admission to the intensive care unit. The Trauma and Injury Severity Score (TRISS) is an established standard for predicting outcomes and benchmarking the quality of trauma care globally. However, the TRISS model has some limitations when used for benchmarking trauma care.