AI Medical Compendium

Uptake of Large Language Models by London Medical Students: Exploratory Qualitative Interview Study.

JMIR formative research Jan 19, 2026

BACKGROUND: The popularity of large language models (LLMs) has grown exponentially across health care. Despite the wealth of literature on proposed applications in medical education, there remains a critical gap regarding their real-world use, benefi... read more

Humans Language Female Young Adult Adult Students, Medical Male Qualitative Research Interviews as Topic London Large Language Models

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.

Epilepsia Jan 19, 2026

OBJECTIVE: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to predict individual seizure risk over time, comparing its performance to traditional clinical predict... read more

Marine natural products.

Natural product reports Jan 19, 2026

Covering: January to the end of December 2024This review covers the literature published in 2024 for marine natural products (MNPs), with 617 citations (578 for the period January to December 2024) referring to compounds isolated from marine microorg... read more

Development and Validation of a Urinary Exosomal miRNA Diagnostic Panel for Early Detection of Esophageal Cancer.

Cancer science Jan 19, 2026

Esophageal squamous cell carcinoma (ESCC) remains a leading cause of cancer-related mortality, with early detection being challenging. Although endoscopic screening can aid in diagnosis, its invasiveness and cost limit widespread compliance. To addre... read more

Automation of Fluorescence-Activated Droplet Release by Deep-Learning-Based Droplet Detector.

IEEE transactions on nanobioscience Jan 19, 2026

Droplet-based microfluidics enables miniaturized, high-throughput biochemical assays but faces challenges in selective droplet retrieval, particularly after long-term monitoring. While light-induced bubble generation offers a promising, hardware-simp... read more

Channel Modeling for Mobile Molecular Communication with Anomalous Diffusion by Deep Neural Network.

IEEE transactions on nanobioscience Jan 19, 2026

Diffusion-based mobile molecular communication (MMC) systems have shown great potential in nanoscale communication, particularly in the scenarios involving anomalous diffusion. Accurately modeling the anomalous diffusion channel of MMC system with mu... read more

EEGMoE: A Domain-Decoupled Mixture-of-Experts Model for Self-Supervised EEG Representation Learning.

IEEE transactions on neural networks and learning systems Jan 19, 2026

Existing deep learning models for electroencephalogram (EEG) are typically tailored for specific tasks, datasets, or even subjects. This specialization restricts their applicability, reducing both perceptual capabilities and overall generalizability.... read more

A General Image Fusion Approach Exploiting Gradient Transfer Learning and Fusion Rule Unfolding.

IEEE transactions on pattern analysis and machine intelligence Jan 19, 2026

The goal of a deep learning-based general image fusion method is to solve multiple image fusion tasks with a single model, thereby facilitating the deployment of models in practical applications. However, existing methods fail to provide an efficient... read more

Classification of Alzheimer's Disease by Modeling Brain Networks as Signed Networks under Deep Learning Frameworks.

IEEE transactions on computational biology and bioinformatics Jan 19, 2026

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of definitive diagnostic tools. This paper introduces an innovative approach to predicting and analyzing Al... read more

Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data.

Neonatology Jan 19, 2026

INTRODUCTION: Retinopathy of prematurity (ROP) remains a leading cause of preventable blindness in preterm infants. This study aimed to develop machine learning (ML) models using non-imaging clinical data to predict ROP, severe ROP (sROP), and treate... read more

Artificial Intelligence Medical Compendium