Artificial Intelligence Medical Compendium

BACKGROUND: Accurate and reproducible assessment of total body surface area (TBSA) in burns is vital for guiding fluid resuscitation, triage, referral decisions, and overall clinical management. Traditional methods are widely used but prone to signif... read more 

This study investigates the global exponential stability of fractional-order Cohen-Grossberg neural networks (CGNN) with impulsive effects. Fractional-order models, which naturally capture memory and hereditary effects, are increasingly used to descr... read more 

Hierarchical reinforcement learning (HRL) with subgoals offers an effective approach to tackling tasks characterized by sparse rewards. HRL enables agents to plan across multiple levels where high levels generate subgoals and low levels pursue to rea... read more 

Rapid advances in social media have made the evolution of public opinion both highly dynamic and complex. An accurate prediction of this evolution is vital for government and corporate decision-making. To address the challenge of predicting the evolu... read more 

The veiled chameleon, Chamaeleo calyptratus, is a model organism in the study of reptile development. The extreme size of its cranial casque relative to other chameleons has been linked to high bite forces and conspecific signaling, yet its unique he... read more 

Lung adenocarcinoma (LUAD) is one of the most prevalent forms of cancer and continues to be associated with high mortality rates, despite recent advances in cancer therapy. Effective risk stratification is critical for guiding treatment decisions and... read more 

OBJECTIVE: To evaluate the impact of combining a second-generation super-resolution deep learning-based reconstruction (DLR2) with a metal artifact reduction (MAR) algorithm in CT images of patients with metal hip implants by assessing both quantitat... read more 

Artificial intelligence and machine learning are becoming increasingly popular. With their deployment comes the challenge of ensuring people have appropriate expectations of their function to ensure calibrated reliance. As these models are traditiona... read more 

In the emergence of pan-drug-resistant bacteria, there is an urgent demand for the discovery of structurally novel antibiotics. While traditional drug development and screening procedures are cumbersome, machine learning models (ML) have shown potent... read more 

Multimodal learning (MML) utilizes the complementary information of different modalities to better handle downstream tasks compared to the uni-modal scenario. However, existing MML methods use all modalities incompatibly: Learning heavily from one do... read more