Artificial Intelligence Medical Compendium

The rapid integration of Artificial Intelligence (AI) into healthcare, particularly clinical nutrition, holds transformative potential for enhanced diagnostics, risk prediction, and personalized therapeutic support. However, many clinicians lack suff... read more 

This study evaluated the utility of deep learning reconstruction (DLR) in vessel wall imaging (VWI) for visualizing the entire cerebral arterial system, including cortical arteries. Seventeen patients underwent post-contrast 3D T1WI-CUBE VWI with 0.5... read more 

Carboxylic acids are fundamental to cellular metabolism and hold broad significance across pharmaceuticals, life science, food science, and agrochemicals. The inherent chirality of many carboxylic compounds profoundly influences their physicochemical... read more 

Deep learning-based multi-view clustering (DMVC) has garnered significant attention and achieved remarkable success, primarily due to its powerful nonlinear representation capabilities. However, most existing DMVC methods fall short in effectively mo... read more 

Offline reinforcement learning trains policies using pre-collected data without interacting with the environment, leading to extrapolation errors and the out-of-distribution (OOD) problem. While existing methods focus on avoiding OOD actions through ... read more 

This note addresses the Kolmogorov-Arnold Representation Theorem (KART) and the Universal Approximation Theorem (UAT), focusing on their frequent misinterpretations found in the neural network literature. Our remarks aim to support a more accurate un... read more 

Multi-bit spiking neural networks (SNNs) have recently become a heated research spot, pursuing energy-efficient and high-accurate AI. However, with more bits involved, the associated memory and computation demands escalate to the point where the perf... read more 

BACKGROUND: To develop and validate a deep convolutional neural network (DCNN) for automated sacroiliitis grading in axial spondyloarthritis (axSpA) using CT images. METHODS: A total of 1,590 patients were enrolled, including 1341 axSpA patients from... read more 

Unsupervised anomaly detection based on knowledge distillation (KD) has been empirically verified to be effective. Nevertheless, previous KD models were constrained in terms of performance and generalization ability due to the similarity of the archi... read more 

Deep learning aims to learn "good" representations that are effective for machine learning tasks, but these representations often lack interpretability due to the black-box nature of neural networks. Disentangled representation learning opts to separ... read more