Artificial Intelligence Medical Compendium

UNLABELLED: This study aims to develop and internally validate separate machine learning models predicting continuous positive airway pressure (CPAP) failure and high-flow nasal cannula (HFNC) failure in preterm neonates. This retrospective cohort st... read more 

The surgical treatment of esophageal cancer is still one of the most demanding surgical procedures with poor long-term survival and considerable perioperative morbidity and mortality. The McKeown and Ivor Lewis procedures have been described decades ... read more 

Cancer remains one of the leading causes of mortality worldwide, and the discovery of novel anticancer drugs continues to be a costly and time-consuming process. Although machine learning (ML) and deep learning (DL) have significantly advanced comput... read more 

The rising incidence of head and neck squamous cell carcinoma (HNSCC) is linked to both the HPV epidemic and the aging population. As a result, there is an emerging need to develop management strategies for older patients with advanced-stage HNSCC. T... read more 

The pharmacokinetics concentration-time profile encodes ADME dynamics. Its optimization in drug discovery curbs late-stage attrition, yet forecasting human pharmacokinetics from chemical structure remains difficult. Machine learning models ignore con... read more 

Inflammatory activation remodels the Golgi apparatus to meet increased secretory demand, yet whether these structural changes carry computationally accessible information about immune cell state has not been explored. Here we show that machine learni... read more 

Background: Artificial intelligence (AI) models for mammography can achieve high diagnostic performance when training and test data originate from similar populations and imaging environments, but performance often deteriorates under cross- cohort do... read more 

Accurate prediction of T cell receptor (TCR) specificity can potentially accelerate development of novel immunotherapies, yet public databases used to train these models contain substantial label noise. This uncertainty complicates model evaluation a... read more 

Most computational methods identify protein-ligand binding sites from ligand-bound (holo) protein structures, where the binding pocket is already preorganized. Although convenient for benchmarking, this setting differs from the practical drug discove... read more 

Invasive candidiasis has become more common in recent decades, particularly within ICUs. Identifying specific Candida spp., and testing their sensitivity to antifungal drugs is crucial for effective treatment that helps healthcare providers to detect... read more