Artificial Intelligence Medical Compendium

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

Showing 60,301 to 60,310 of 228,072 articles

Multimodal analytical approaches to nanomaterials: TEM, diffraction, image processing, and fractal analysis.

The Analyst Jan 19, 2026
In the field of materials science, using diverse experimental and computational methods is a well-known approach as the most effective route to comprehensive material characterization. Combining high-resolution transmission electron microscopy (TEM) ... read more 

Machine Learning Prediction of Chronic Kidney Disease in Elderly MetS Patients Using NHANES 2011-2020 Data.

Rejuvenation research Jan 19, 2026
BACKGROUND: Chronic kidney disease (CKD) is getting more common in elderly people with metabolic syndrome, but early detection and risk prediction are still hard. So we created and validated a CKD risk prediction model tailored for these patients to ... read more 

Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study.

Mayo Clinic proceedings. Digital health Jan 19, 2026
OBJECTIVE: To evaluate the effectiveness of the artificial intelligence-based qXR lung nodule malignancy score (qXR-LNMS) in detecting high-risk incidental pulmonary nodules (IPNs) on chest X-rays (CXRs). PATIENTS AND METHODS: The CREATE (NCT05817110... read more 

Radiographic Data Segmentation as a Tool in Machine Learning and Deep Learning Artificial Intelligence Algorithms.

Dental clinics of North America Jan 19, 2026
This study reviews radiographic data segmentation as a cornerstone of machine learning (ML) and deep learning (DL) in dentistry. After outlining artificial intelligence (AI), ML, and DL concepts, it highlights convolutional neural networks-driven tas... read more 

Generating Structurally Diverse Therapeutic Peptides with GFlowNet

bioRxiv Jan 19, 2026
Reinforcement learning approaches for therapeutic peptide generation suffer from mode collapse, converging to narrow regions of sequence space even when explicit diversity penalties are applied. Fine-grained analysis reveals persistent mode-seeking b... read more 
bioinformatics

High-PepBinder: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design

bioRxiv Jan 19, 2026
Peptides, as therapeutic molecules, offer unique advantages in targeting complex protein surfaces, yet their rational design remains limited by the vastness of the sequence space and the constraints of traditional approaches. Here, we propose High-Pe... read more 
bioinformatics

dgiLIT: A Method for Prioritization and AI Curation of Drug-Gene Interactions

bioRxiv Jan 19, 2026
IMPORTANCE: The Drug-Gene Interaction Database (DGIdb) has a long history of driving hypothesis generation for biomedical research through the careful curation of drug-gene interaction data from primary and secondary sources with supporting literatur... read more 
bioinformatics

A predicted cancer dependency map for paralog pairs

bioRxiv Jan 19, 2026
Background Genome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identification of genetic vulnerabilities associated with specific biomarkers. Paralogs, despite being common dr... read more 
cancer biology

Accuracy of Artificial Intelligence-Based Models versus Traditional Scoring Systems (APACHE, SOFA, SAPS) for Predicting Mortality in ICU Patients: A Systematic Review and Meta-Analysis

medRxiv Jan 19, 2026
Introduction: Reliable estimation of mortality among critically ill patients is crucial for guiding clinical decisions and optimizing ICU performance. Traditional scoring systems such as APACHE, SOFA, and SAPS are commonly applied, though their predi... read more 
intensive care and critical care medicine

Using Artificial Intelligence to Assess Treatment-Effect Heterogeneity in Pragmatic Cardiovascular Trials: Insights from TRANSFORM-HF

medRxiv Jan 19, 2026
Background and Aims: Pragmatic clinical trials are designed to assess interventions in real-world settings, and their broad inclusion criteria and clinical variability create valuable opportunities for exploring heterogeneity of treatment effects. In... read more 
cardiovascular medicine
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