Artificial Intelligence Medical Compendium

Diffusion Models (DMs) stand out among generative models for their impressive capabilities, but their application is often constrained by slower inference speeds and higher computational costs. Previous works have attempted to address these issues th... read more 

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed super... read more 

This paper investigates the distributed variational generalized Nash equilibrium (vGNE) seeking problem for nonsmooth noncooperative game. Specifically, a distributed vGNE-seeking neurodynamic approach (vGSNA) modeled by disturbed Euler-Lagrange (EL)... read more 

OBJECTIVE: Accurate prediction of type 2 diabetes mellitus (T2DM) onset is critical to enable timely interventions and preventive strategies. Although machine learning (ML) approaches have shown promise in risk prediction, their complexity often limi... read more 

Sparse-view computed tomography (SVCT) significantly reduces patient radiation exposure and accelerates data acquisition by using fewer projections for tomographic reconstruction. However, reconstructed images often suffer from severe artifacts, whic... read more 

This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random disturbance model of the cortical-thalamus loop, the neural modulation problem is successfully transformed... read more 

Oral cancer often develops from oral potentially malignant disorders. Oral leukoplakia (OL) is the most common oral potentially malignant disorder. However, not all patients with OL develop oral cancer in their lifetimes, and cancer risk assessment i... read more 

OBJECTIVES: The aim of this study was to evaluate the interproximal surfaces of posterior teeth using panoramic and bitewing radiographs, and to compare the diagnostic accuracy of dental interns with that of an artificial intelligence application. ME... read more 

PURPOSE: This study aims to develop an automated framework for operator-independent assessment of cardiac ventricular function from highly accelerated images. METHODS: We introduce a deep learning framework that generates reliable ventricular volumet... read more 

ObjectiveThe current study aimed to explore the impacts of experiencing superior behaviors-accumulating large amounts of evidence and high automation use rates-on subsequent evidence accumulation rates and adaptable (discretionary) automation use dec... read more