Artificial Intelligence Medical Compendium

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

Showing 1,351 to 1,360 of 200,346 articles

Hospitalists Are Already Using AI-Why Implementation Will Determine Its Impact.

Journal of medical Internet research Jun 1, 2026
The adoption of artificial intelligence (AI) into clinical practice is accelerating, outpacing the development of organizational guidance, training, and governance. A recent study indicated that two-thirds of hospitalists are using AI, particularly l... read more 

Improving Vancomycin Therapeutic Drug Monitoring With a Deep Learning-Based Two-Compartment Predictive Model: Development and Validation Study.

JMIR AI Jun 1, 2026
BACKGROUND: Vancomycin is a widely used antibiotic that requires therapeutic drug monitoring (TDM) for optimized individual dosage. A deep learning-based model, pharmacokinetic recurrent neural network-1 compartment model (PKRNN-1CM), has shown the a... read more 

3D object detection for vehicle-mounted LiDAR based on deep learning and euclidean clustering algorithm.

PloS one Jun 1, 2026
Object Detection (OD) stands as a fundamental task in the area of autonomous driving environment perception. This study introduces a 3D OD method grounded in deep learning and an improved Euclidean clustering algorithm, aiming to improve the accuracy... read more 

FreeTune4D: Anatomy-Aware 4D-MRI Motion Reconstruction Benchmark and Free Fine-Tuning Framework.

IEEE journal of biomedical and health informatics Jun 1, 2026
Deep learning and prior-image-guided cross modality motion reconstruction methods have recently en abled faster acquisition time and lower artifacts in abdominal four-dimensional magnetic resonance imaging (4D MRI). However, the limited availability ... read more 

Development of autoencoder-guided attention-LSTM models for predicting nocturnal hypoglycemia risk in Type 1 Diabetes.

IEEE transactions on bio-medical engineering Jun 1, 2026
OBJECTIVE: Nocturnal hypoglycemia (NH) is a major, often undetected risk for individuals with Type 1 Diabetes (T1DM). Current prediction models lack sufficient lead time for proactive, pre-sleep interventions. This study develops a novel, interpretab... read more 

Physical Parameter-Guided Deep Learning Ultrasound Localization Microscopy Framework Based on Diffusion Model.

IEEE transactions on bio-medical engineering Jun 1, 2026
OBJECTIVE: Deep learning based-imaging methods have demonstrated significant potential for achieving high spatiotemporal resolution in Ultrasound Localization Microscopy (ULM), a state-of-the-art hemodynamic microvascular imaging method. The accurate... read more 

Liver Nodule Anomaly Detection Using Ultra-sound Radiofrequency Signals and Variational Autoencoders.

IEEE transactions on bio-medical engineering Jun 1, 2026
OBJECTIVE: Detection of liver nodules in ultrasound (US) is challenging due to the low visibility in the presence of steatotic and cirrhotic livers. Anomaly detection approaches offer an unsupervised alternative by modeling "normal tissues" and ident... read more 

HiRMD: A System for Mortality Prediction via LLM-Based High-Risk Information Extraction and Diagnosis.

IEEE transactions on bio-medical engineering Jun 1, 2026
OBJECTIVE: To improve in-hospital mortality prediction from longitudinal electronic health records (EHRs) by extracting mortality-related high-risk clinical information and integrating heterogeneous clinical evidence. METHODS: We propose HiRMD, an LL... read more 

ConvShareViT: A Vision Transformer-Like Architecture for Free-Space Optical Accelerators.

IEEE transactions on neural networks and learning systems Jun 1, 2026
This article introduces convolutional shared vision transformers (ConvShareViT), a novel deep learning architecture that adapts the vision transformer (ViT) architecture to the 4f free-space optical system. ConvShareViT replaces linear layers in mult... read more 

Toward Fair Federated Graph Learning.

IEEE transactions on neural networks and learning systems Jun 1, 2026
As a privacy-preserving collaborative paradigm, federated graph learning (FGL) enables distributed training of graph neural networks (GNNs) without exposing raw graph data. Subgraph-FL has become the dominant FGL paradigm, yet most studies focus on o... read more 
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