Artificial Intelligence Medical Compendium

Somatic evolution leads to clonal heterogeneity, which fuels cancer progression and therapy resistance. To decipher the consequences of clonal heterogeneity, we require a method that deconvolutes complex clonal architectures and their downstream tran... read more 

Regulation of transcription initiation is the ground level of modulating gene expression during plant development. This process relies on interactions between transcription factors and cis-regulatory elements (CREs), which become promising targets fo... read more 

BACKGROUND CONTEXT: Radiomics, a technique employing machine learning (ML) to extract quantitative features from processed radiographic images, holds promise for improving clinical prediction models. It offers the potential to comprehensively charact... read more 

BACKGROUND AND AIMS: Autonomous mobile robots (AMRs) have an increasingly wide range of medical applications. However, their use in endoscopy centers remains underdeveloped. This study evaluated the efficiency and cost-effectiveness of AMR-assisted e... read more 

BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-scale neural networks. Methamphetamine use disorder (MUD) and obsessive-compulsive disorder (OCD) exh... read more 

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BACKGROUND: Thrombophilia evaluation requires integration of biochemical findings with clinical history, much of which is embedded in unstructured electronic health record (EHR) text. Manual chart review is labour-intensive and prone to omissions. Na... read more 

Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two critical cardiovascular diseases, where accurate diagnosis is essential for timely intervention. Although deep learning technology has greatly improved the diagnostic accuracy of ... read more 

Ovarian cancer is a major health concern for women, contributing to substantial mortality and morbidity. Timely identification of ovarian cancer is crucial for enhancing patient health and survival rates. Current diagnostic practices involve the manu... read more 

Anti-inflammatory peptides (AIPs) represent promising therapeutic agents, however, their discovery via traditional experimental methods is constrained by low throughput and high costs. Current computational prediction methods face persistent challeng... read more