Artificial Intelligence Medical Compendium

Foundation models (FM) offer a promising alternative to supervised deep learning (DL) by enabling greater flexibility and generalizability without relying on large, labeled datasets. This study investigates the performance of supervised DL models and... read more 

High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions for glioma patients. However, due to inherently low metabolite concentrations and signal-to-noise r... read more 

Protein-protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-molecule inhibitors. Traditional computational approaches rely heavily on existing chemical librarie... read more 

With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developing predictive models of drug responses and identifying associated biomarkers. However, many currently... read more 

Dynamic graph representation learning recently garnered enormous research attention. Despite the notable successes of existing methods, they usually characterize dynamic graphs as a perceptual whole and learn dynamic graph representations within an e... read more 

Cancer presents a significant challenge in the field of public health due to its high incidence, mortality rate, and inherent heterogeneity. Integrating multi-omics biological data offers a comprehensive and intricate understanding of biological proc... read more 

Graph neural networks (GNNs) are gaining popularity for processing graph data. In real-world scenarios, graph data within the same dataset can vary significantly in scale. This variability leads to depth-sensitivity, where the optimal depth of GNN la... read more 

PURPOSE: This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a particular focus on the artificial intelligence-based TIRADS (AI-TIRADS), in characterizing thyroid no... read more 

OBJECTIVE: This study explored the feasibility of preoperatively predicting perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) through machine learning based on clinical and CT image features, which may help in individualized clinical... read more 

Artificial intelligence (AI), particularly machine learning (ML), is increasingly influencing pharmacovigilance (PV) by improving case triage and signal detection. Several studies have reported encouraging performance, with high F1 scores and alignme... read more