Artificial Intelligence Medical Compendium

Refining coupled multiphysics fields in pollutant transport from sparse measurements is critical for environmental risk assessment and industrial pollution mitigation. However, existing numerical solvers and machine learning architectures exhibit inh... read more 

BACKGROUND: Large language models (LLMs) demonstrate significant potential in medical information provision and may serve as valuable tools for patients seeking health information. Existing research primarily focuses on individual models or general m... read more 

BACKGROUND: Cerebral palsy (CP), a leading cause of childhood motor disability, severely impacts lower limb function. Conventional gait training (CGT) faces challenges in standardization, while robot-assisted gait training (RAGT) offers biomechanical... read more 

In recent years, graph neural networks (GNNs) have made remarkable achievements in graph representation learning, mainly by exploring the correlation between input graphs and labels. However, some studies have shown that GNN tends to predict the labe... read more 

Postprandial hyperglycemia is a key driver in the development of type 2 diabetes, and dietary starch is a major modulator of glycemic response. The crystalline structure of starch is a principal determinant of its digestibility and postprandial glyce... read more 

Emotion recognition has broad application prospects in real life. The variability across subjects in emotion-related electroencephalogram (EEG) signals is still a significant challenge for the practical use of EEG-based emotion recognition despite re... read more 

BACKGROUND: Biliary stent placement during endoscopic retrograde cholangiopancreatography (ERCP) is important for drainage in common bile duct (CBD) strictures, while the stent length is associated with many stent-related complications. We aimed to d... read more 

Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serv... read more 

Real-world objects usually inherently possess rich and complex structures that are difficult to model using a single graph or instance-based representation. Multi-Graph Learning (MGL) addresses this by representing each object as a bag of graphs, eac... read more 

The technique of structural re-parameterization has been widely adopted in Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) for image-related tasks. However, its integration with attention mechanisms in the video domain remains... read more