Artificial Intelligence Medical Compendium

Machine learning exhibits notable advantages in simulating complex thermochemical processes such as gasification, with its robust nonlinear fitting capabilities effectively capturing the coupling relationships among multiple variables. In this study,... read more 

Understanding the association between structural properties of the human brain and individual differences in behavior is an ongoing endeavor, challenged by the brain's complexity. Past approaches, limited by simplistic neural structure measures like ... read more 

INTRODUCTION: Learners are rapidly using generative artificial intelligence (AI) models in their education. We assessed the performance of recently released or updated models on the 2024 American Academy of Orthopaedic Surgeons Orthopaedic In-trainin... read more 

BACKGROUND: A 24-hour urine collection is central to the metabolic evaluation and prevention of nephrolithiasis. Despite its widespread use, methodological inconsistencies in data reporting and analysis limit the reliability, reproducibility, and cli... read more 

Accurate brain tumor segmentation from multi-modal MRI is critical for clinical diagnosis and treatment planning. However, effectively exploiting the complementary information across different modalities remains challenging due to modality-specific n... read more 

Fentanyl analogs' (FA) rampant proliferation causes widespread abuse, fatalities, and severe social issues globally. Their high toxicity, rapid metabolism, and poor detectability make them a major anti-drug challenge. This study aimed to establish an... read more 

Radical-mediated advanced oxidation and/or reduction processes (AOPs/ARPs) have shown remarkable efficacy in degrading organic pollutants in wastewater, accurate prediction of radical-pollutant reaction kinetic and thorough mechanistic understanding ... read more 

Disinfection by-products (DBPs) remain a major health concern in drinking water, but unified prediction across diverse species with mechanistic clarity is still difficult. Current approaches also struggle with limited dataset size, lack of interpreta... read more 

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investigate the application of deep learning methods to electroencephalogram (EEG) signals to predict clinica... read more 

Accurate reconstruction of three-dimensional acoustic fields from sparse multi-frequency measurements is essential for engineering tasks such as cabin noise control, building-acoustics optimization, and machinery diagnostics. In this study, a frequen... read more