Artificial Intelligence Medical Compendium

Microsatellite instability (MSI) has gained increasing attention as a promising biomarker for cancer immunotherapy in diverse cancer types. As a result of DNA mismatch repair (MMR) deficiency, MSI tumors show a hypermutator phenotype. Based on this f... read more 

OBJECTIVES: The aim of this study was to evaluate the impact of introducing an autonomous courier in a clinical laboratory, focusing on specimen turnaround time (TAT). We assessed whether high-frequency robotic transport from the central accessioning... read more 

Graph Neural Networks (GNNs) deployed in high-stakes applications pose concerns about fairness and privacy. Current methods typically address these issues separately, ignoring the inherent conflict: enforcing individual fairness via distance-based me... read more 

Although most pollutants are removed during water treatment, residual heavy metals can enter drinking water distribution systems (DWDSs), posing a significant challenge for ensuring drinking water safety. This study investigated the levels of Mn, Al,... read more 

Emotional states are fundamental attributes distinguishing humans from machines, and productivity represents one of the primary life objectives for this emotionally driven being. However, existing research on productivity and job performance frequent... read more 

OBJECTIVE: This study presents an independent clinical evaluation of Dr.Noon CVD, a commercially developed artificial intelligence (AI)-based retinal imaging model that estimates cardiovascular disease (CVD) risk. We assessed whether the model can ef... read more 

BACKGROUND: Anemia is one of the most common hematological disorders causing fatigue, increased vulnerability to infection, low birth weight, and threatening the physical and mental development of children. Hence, the early identification reduces the... read more 

Twisted Convolutional Networks (TCNs) are proposed as a novel deep learning architecture for classifying one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike conventional Convolutional Neural Networks (CNNs) tha... read more 

Pedestrian attribute recognition (PAR) aims to achieve robust prediction of multiple binary attributes in pedestrian images. Traditional PAR methods employ joint learning of a backbone network to extract shared features and a linear classifier to pre... read more 

PURPOSE: To evaluate the image quality of three reconstruction methods-filtered back projection (FBP), adaptive statistical iterative reconstruction (AR50), and deep learning-based reconstruction (DL-M)-processed with the Smart Metal Artifact Reducti... read more