AI Medical Compendium

Exploratory analysis of the associations of the brain age gap with cognitive function and amyloid-β accumulation: participants selection based on metabolic and physiological blood markers.

Neurobiology of aging Mar 11, 2026

The brain age gap (BAG) is defined as the difference between brain age estimated from MRI using artificial intelligence and chronological age, and has been proposed as a biomarker reflecting aging and neurodegeneration. However, the association betwe... read more

Mathematical modelling and analysis of human metapneumovirus transmission dynamics using neural network intelligence and optimal control.

Computational biology and chemistry Mar 11, 2026

Human metapneumovirus (hMPV) is a serious global health threat because it causes human respiratory diseases in people of all ages. The complicated dynamics of this virus transmission exist in complications of waning immunity and reinfection that are ... read more

Mathematical fidelity vs. perceptual realism: A multi-center study of super-resolution radiomics for lung adenocarcinoma invasiveness.

European journal of radiology Mar 11, 2026

OBJECTIVE: To identify the optimal super-resolution (SR) architecture for radiomics by comparing three models (Residual Channel Attention Network (RCAN), Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), and Hybrid Attentio... read more

A regional classification framework integrating AI and causal inference revealing the drivers of lake eutrophication in China.

Water research Mar 11, 2026

Lake eutrophication is a globally pervasive environmental issue. While its driving mechanisms exhibit significant spatial heterogeneity, the relative contributions of climate change versus anthropogenic pressure remain underexplored at large spatial ... read more

Rethinking "Useful" and "Useless" AI in radiology.

Current problems in diagnostic radiology Mar 11, 2026

Artificial Intelligence (AI) is reaching a pivotal moment in radiology, with rapidly expanding applications that often overwhelm clinicians and fuel skepticism or fear of missing out. This editorial proposes a simplified, practice-oriented framework ... read more

Machine learning-based quantification of neurovascular compression for correlation with trigeminal neuralgia pain outcomes.

Pain Mar 11, 2026

Machine learning-generated segmentations of the trigeminal nerve and surrounding vasculature can quantitatively assess the magnitude of neurovascular compression (NVC) in patients with trigeminal neuralgia (TN). Using the magnetic resonance imaging (... read more

Automated auditing of emergency department documentation using large language models.

The American journal of emergency medicine Mar 11, 2026

INTRODUCTION: Errors in emergency department (ED) documentation can lead to patient harm and medicolegal risk, however manual document auditing is resource-intensive and difficult to scale. Large language models (LLMs) may offer an automated alternat... read more

Transient frontal spectral events from EEG predict antidepressant response to sertraline in depression.

Journal of psychiatric research Mar 11, 2026

Resting-state scalp electroencephalography (EEG) is a promising method for predicting patient outcomes of antidepressant treatments. Machine-learning-based EEG analyses of averaged power features (APF) have predicted antidepressant responders in stan... read more

MD-LSM: an enabling tool for real-time monitoring linear separability of hidden-layer outputs of deep networks.

Neural networks : the official journal of the International Neural Network Society Mar 11, 2026

The linear separability of hidden-layer outputs plays a key role in understanding the working mechanism of deep networks. However, it is still challenging to develop the linear separability measure (LSM) that satisfies the following requirements: 1) ... read more

Shapley value optimized differentiable architecture search for lightweight neural networks in resource-constrained environments.