Artificial Intelligence Medical Compendium

Designing molecules that must satisfy multiple, often conflicting, objectives is a central challenge in molecular discovery. The enormous size of the chemical space and the cost of high-fidelity simulations have driven the development of machine lear... read more 

Food microorganisms act as a double-edged sword in production and storage. Beneficial microbes create distinctive flavor, texture, and nutritional value, whereas harmful microbes threaten food safety and shelf life. Their survival and adaptation to a... read more 

BACKGROUND: Complex matrix effects and spectral interference severely limit the capability of laser-induced breakdown spectroscopy (LIBS) for quantitative analysis of rare earth elements in uranium polymetallic ores. A multi-scale dual-attention conv... read more 

Through new and astonishing capabilities of artificial intelligence, the editors of The American Surgeon have been able to step back in time and conduct a virtual interview with the most eponymous surgeon in history: Allen Oldfather Whipple. We caugh... read more 

Metal-organic frameworks (MOFs) have attracted considerable attention owing to their multifaceted applications and structurally tunable characteristics. Powder X-ray diffraction (XRD) is an essential technique for high-throughput characterization of ... read more 

The development of empirical materials, hindered by the complex interplay between material properties and reaction mechanisms, typically necessitates an extensive trial-and-error process to identify optimal catalysts. In our study, we integrate densi... read more 

Radiomics is increasingly explored as a tool for improving diagnosis, prognosis, and treatment planning. However, concerns exist about the reproducibility and methodological rigor of its studies. The integration of high-dimensional radiomic features ... read more 

OBJECTIVES: Transcatheter aortic valve implantation (TAVI) is crucial for treating calcified aortic stenosis (CAS), yet post-procedural outcomes remain variable. The CAPRI score previously showed promising results in predicting 1-year all-cause morta... read more 

PURPOSE: Abdominal radiography (AXR) is routinely performed in emergency departments (ED) but has limited clinical utility. Thus, this study developed deep-learning models using AXR to predict hospitalization in ED patients with abdominal symptoms. M... read more 

OBJECTIVE: This study aimed to develop and evaluate deep learning-based models for estimating CIELAB tooth color coordinates from standardized extraoral photographs, using in vivo spectrophotometric measurements as the reference standard, to provide ... read more