Artificial Intelligence Medical Compendium

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 

Recent advancements in microbiome research have revealed the restorative capacities of postbiotics, including indole and its derivatives generated via bacterial tryptophan metabolism. Widespread in prokaryotic and eukaryotic communities, indole and i... read more 

OBJECTIVES: To evaluate the performance of Claude 3.7 Sonnet in automating data extraction for systematic literature reviews (SLRs). METHODS: An artificial intelligence (AI) extraction model based on the Claude 3.7 Sonnet large language model was dev... read more 

RATIONALE AND OBJECTIVES: Large language models (LLMs) are increasingly investigated in radiology education. This study evaluated the performance of several advanced LLMs on radiology residency in-training examination questions, with a focus on wheth... read more 

BACKGROUND: The American Academy of Ophthalmology recommendations on screening for hydroxychloroquine (HCQ) retinopathy are now a decade old. This revision provides an update based on current knowledge. Major points and changes are as follows. NATURE... read more 

BACKGROUND: Depression presents with heterogeneity in symptom trajectories, complicating individualized treatment. Identifying distinct classes of symptom trajectories and their predictors may enable earlier intervention for those at greatest risk of... read more 

BACKGROUND: Sex-related differences in coronary artery disease (CAD) burden and outcomes are increasingly recognized but not fully understood, particularly when assessed using advanced imaging techniques. OBJECTIVES: To investigate sex differences in... read more 

Single-cell multimodal analysis based on machine learning integrates data from multiple modalities such as cell morphological features, gene expression, proteomics, and metabolomics and observes single cells from multiple dimensions. This multi-dimen... read more 

This comprehensive review focuses on the use of knowledge graphs and Graph Neural Networks (GNNs) to integrate multi-omics data in the field of personalized nutrition. It investigates how different biological datasets-like genomics, proteomics, metab... read more