Artificial Intelligence Medical Compendium

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

Showing 48,781 to 48,790 of 224,513 articles

Understanding the Fine-Grained Knowledge Capabilities of Vision-Language Models

arXiv Feb 19, 2026
Vision-language models (VLMs) have made substantial progress across a wide range of visual question answering benchmarks, spanning visual reasoning, document understanding, and multimodal dialogue. These improvements are evident in a wide range of VL... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG cs.MM

Machine Learning Based Prediction of Surgical Outcomes in Chronic Rhinosinusitis from Clinical Data

arXiv Feb 19, 2026
Artificial intelligence (AI) has increasingly transformed medical prognostics by enabling rapid and accurate analysis across imaging and pathology. However, the investigation of machine learning predictions applied to prospectively collected, standar... read more 
Machine Learning cs.AI

Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget

arXiv Feb 19, 2026
Data collection is a critical component of modern statistical and machine learning pipelines, particularly when data must be gathered from multiple heterogeneous sources to study a target population of interest. In many use cases, such as medical stu... read more 
Machine Learning cs.LG math.ST

MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis

arXiv Feb 19, 2026
Self-supervised learning (SSL) and diffusion models have advanced representation learning and image synthesis. However, in 3D medical imaging, they remain separate: diffusion for synthesis, SSL for analysis. Unifying 3D medical image synthesis and an... read more 
Image and Video Processing cs.CV cs.GT

NPHS2 Revisited Through 208 Cases and Podocin Complex Modeling.

Kidney international reports Feb 19, 2026
INTRODUCTION: Steroid-resistant nephrotic syndrome (SRNS) is the leading cause of chronic glomerular disease in individuals under 25 years of age. Biallelic variants in NPHS2, encoding podocin, are the most common monogenic etiology. Podocin homo-oli... read more 

Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

Pediatric neurology Feb 19, 2026
BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiveness remain underdeveloped. This study evaluated various machine learning (ML) models in predicting r... read more 

Advanced Real-Time, Non-Destructive Spectral Fingerprinting for Early microbial Spoilage Detection: AI-Integrated Raman Biosensing Platform for Scalable Food Safety and Quality.

Food research international (Ottawa, Ont.) Feb 19, 2026
Food spoilage poses a global challenge, contributing to economic losses, food insecurity, and health risks from microbial contamination. Conventional detection methods are often destructive, time-consuming and ineffective at identifying early biochem... read more 

Rapid geographical traceability and quality identification of black tea via integration of ambient mass spectrometry and machine learning.

Food research international (Ottawa, Ont.) Feb 19, 2026
Accurate geographical traceability and comprehensive adulteration assessment of black tea are essential for quality control and market regulation, but remain challenging due to subtle metabolic differences and complex adulteration practices. In this ... read more 

A theory-guided multi-scale temporal fusion network for road surface temperature nowcasting.

iScience Feb 19, 2026
The durability of road infrastructures strongly correlates with thermal variations. This study proposed a theory-guided multi-scale temporal fusion network (MSTF-Net), integrating multi-scale temporal feature pyramids, dual-pooling mechanisms, and re... read more 

Non-invasive prediction of Ki-67 expression in gastric cancer using AI-based dual-energy CT: a multicenter study.

European journal of radiology Feb 19, 2026
OBJECTIVE: To develop and validate a machine learning model based on quantitative parameters of dual-energy CT (DECT) virtual monoenergetic images (VMIs) for the noninvasive preoperative prediction of Ki-67 expression status in gastric cancer. METHOD... read more 
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