Artificial Intelligence Medical Compendium

Artificial intelligence (AI) holds significant promise as a diagnostic and therapeutic adjunct, and it is being rapidly employed in health care. Implementation, however, has been fraught with ethical challenges, with several unique to the field of su... read more 

Cardiomyopathies and heart failure (HF) represent a diverse group of cardiac conditions that significantly impact global health. The increasing complexity of these disorders demands more precise tools for diagnosis, risk assessment, and treatment gui... read more 

BACKGROUND: Generative Artificial Intelligence(GenAI) significantly enhances medical research efficiency but raises ethical concerns regarding research integrity. The lack of systematic guidelines for its ethical use underscores the need to investiga... read more 

Next-generation sequencing has improved diagnostic outcomes for neuromuscular disorders, but interpreting rare missense variants remains challenging. We evaluated AlphaMissense, a recently developed machine learning tool, for predicting missense vari... read more 

Breast cancer is the leading cause of cancer-related deaths among women worldwide. Early detection through mammography significantly improves outcomes, with breast density acting as both a risk factor and a key interpretive feature. Although the Brea... read more 

The growing heterogeneity of cardiac patient data from hospitals and wearables necessitates predictive models that are tailored, comprehensible, and safeguard privacy. This study introduces PerFed-Cardio, a lightweight and interpretable semi-federate... read more 

Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 2020. Current diagnostic approaches, such as prostate-specific antigen (PSA) testing and trans-rectal u... read more 

Increases in impulsivity and negative affect (e.g., neuroticism) are common during adolescence and are both associated with risk for alcohol-use initiation and other risk behaviors. Whole-brain functional connectivity approaches-when coupled with app... read more 

This study aimed to develop an AI-based diagnostic model for Hirschsprung's disease (HD) using deep learning on contrast enema (CE) images, with the goal of improving diagnostic accuracy while reducing invasiveness. The dataset included 725 CE images... read more 

PURPOSE: Accelerating magnetic resonance acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is com... read more