Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 20,691 to 20,700 of 216,088 articles

Predicting 28-day all-cause mortality in critically ill ischemic stroke patients using white blood cell-to-hemoglobin ratio and dual-feature selection machine learning models.

Expert review of molecular diagnostics May 11, 2026
BACKGROUND: The white blood cell-to-hemoglobin ratio (WHR) is a composite biomarker of inflammation and nutrition, but its prognostic role in critically ill ischemic stroke (IS) patients is unclear. METHODS: A cohort of 3,112 patients from MIMIC-IV w... read more 

Low-Field Musculoskeletal MRI: Recent Advances, Challenges, and Practice Considerations.

Journal of computer assisted tomography May 11, 2026
Magnetic resonance imaging (MRI) is well established as a cornerstone of musculoskeletal imaging. Although most clinical musculoskeletal MRI examinations are performed at 1.5 T and 3 T, recent advancements in low-field MRI have enabled imaging at 0.5... read more 

Assessing the Reliability of MRI and CT Scans for Liver Volume Estimation in Patients Undergoing Liver Transplantation.

Journal of computer assisted tomography May 11, 2026
OBJECTIVE: Magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used to measure organ volumes, but accuracy has not been methodically confirmed. This research seeks to fill this gap by comparing preoperative MRI and CT for liver... read more 

Zwitterionic Lipid Nanotherapeutics from Mulberry for Oral Treatment of Diabetic Colitis.

ACS nano May 11, 2026
Diabetic colitis is a severe gastrointestinal complication of type 2 diabetes, which presents the key pathophysiological hallmarks of hyperglycemia, intestinal barrier disruption, immune dysregulation, and microbial metabolic imbalance, posing signif... read more 

Glass-box agentic-style workflow for multiclass cine cardiac magnetic resonance imaging classification with a large language model.

Diagnostic and interventional radiology (Ankara, Turkey) May 11, 2026
PURPOSE: To develop and evaluate a glass-box, agentic-style radiology pipeline that separates perception from reasoning for auditable multiclass diagnosis on cine cardiac magnetic resonance imaging (MRI), and to quantify accuracy, robustness across d... read more 

M-protein interference in laboratory diagnostics: a comprehensive narrative review of mechanisms and management strategies.

Clinical chemistry and laboratory medicine May 11, 2026
Monoclonal proteins (M-proteins) serve as critical biomarkers for plasma cell dyscrasias, yet their unique physicochemical properties paradoxically compromise the accuracy of routine laboratory diagnostics. Beyond causing well-documented anomalies in... read more 

Using artificial intelligence as a mindtool to foster critical thinking in biology education.

Journal of microbiology & biology education May 11, 2026
Artificial intelligence tools are increasingly accessible and have the potential to offer learners innovative ways to promote deep learning and engagement with academic content. These tools can be used to support diverse learners, improve academic su... read more 

Teaching medical students to learn effectively in the age of AI.

Medical teacher May 11, 2026
What was the educational challenge? Medical students are increasingly using artificial intelligence (AI) tools for studying, including for summarization, explanation, practice question generation, and tutoring. While these tools expand access to lear... read more 

Development of an Interpretable Deep Learning-Based Segmentation Algorithm for Automated Assessment of Oral Diadochokinesis in Progressive Neurological Diseases.

Journal of speech, language, and hearing research : JSLHR May 11, 2026
PURPOSE: We aimed to develop a universal, fully automated segmentation algorithm that allows robust analysis of oral diadochokinesis across various neurological diseases, dysarthria types, and dysarthria severities. METHOD: Recordings of sequential m... read more 

AI-driven Abdominal Aortic Calcification Extracted From Contrast-enhanced CT Is Predictive of All-cause Mortality and Cardiovascular Events in a Large Adult Population.

Journal of computer assisted tomography May 11, 2026
OBJECTIVE: To assess the utility of AI-driven quantification of abdominal aortic calcification (AAC) extracted from contrast-enhanced CT (CECT) scans using a fully automated AI tool for predicting all-cause mortality and cardiovascular events (CVEs).... read more 
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