Artificial Intelligence Medical Compendium

OBJECTIVES: Fatal road traffic collisions account for a small proportion of reported crashes but impose a disproportionate societal and public health burden. This study evaluates interpretable machine-learning models and proposes a consensus machine ... read more 

OBJECTIVE: Interpretive clinical artificial intelligence (AI), in which a clinician interprets an image or signal and an AI system offers a competing or confirming read, is increasingly routine in radiology, pathology, dermatology, and endoscopy. Ove... read more 

OBJECTIVE: To evaluate whether routine immune-inflammatory biomarkers predict no-reflow (NR) after percutaneous coronary intervention (PCI) in acute myocardial infarction (AMI) patients, quantify their incremental predictive value over clinical varia... read more 

BACKGROUND: Postpolypectomy surveillance intervals derived from colonoscopy findings and histopathology should be guideline-based, yet these are frequently not adhered to, contributing to under- and oversurveillance. This motivates a more structured ... read more 

Inferring gene regulatory networks (GRNs) is crucial for understanding the complex regulatory relationships that govern gene expression in cells. Recently, several computational methods have been proposed for reconstructing GRNs from single-cell RNA ... read more 

With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed tomography (CT) denoising. However, they require paired noisy-clean training data, which is diffic... read more 

Cardiovascular disease (CVD) remains the leading cause of death worldwide. While clinical scores provide modest predictive accuracy, the electrocardiogram (ECG) is rarely leveraged for prognosis. Artificial intelligence (AI) and deep learning now ena... read more 

The deepening integration of generative AI into design education is driving a paradigm shift in human-computer AI collaboration (HAIC) -from mere tool assistance toward intelligent synergy. However, its dual impact on the competency development of de... read more 

The energy landscape picture is a central tool to study many-body systems. In particular, the energy landscapes of glass-forming liquids, jammed packings, constraint satisfaction problems, or neural networks contain a plethora of minima corresponding... read more 

As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on the predictability, computability, and stability (PCS) principles for ve... read more