Artificial Intelligence Medical Compendium

Wildfire fine particulate matter (PM2.5) is an emerging health concern, yet its effects on mosquito-borne diseases, particularly dengue, remain unclear. Brazil faces one of the world's highest dengue burdens alongside frequent wildfires. We examined ... read more 

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is marked by dopaminergic neuron loss and α-synuclein aggregation. Although Braak staging based on Lewy body pathology stratifies disease progression, clinical variabilit... read more 

Poultry consumption has surged in recent years, raising food safety concerns from foreign material (FM) contamination, particularly transparent plastics that are difficult to detect using conventional methods like X-ray imaging. This study aims to de... read more 

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INTRODUCTION: Assessing the interaction between the body's center of mass (COM) and center of pressure (COP) during gait provides valuable insights into dynamic balance control. However, traditional motion capture methods are costly and impractical i... read more 

Simulating urban drainage hydraulics is computationally demanding, limiting its application in tasks that require real-time or repeated simulations. Graph Neural Networks (GNNs) are promising metamodels, but the effect of their internal components an... read more 

Image harmonization is a crucial task in image composition and editing, which aims to adjust the foreground to be harmonious with the background. Due to the lack of clear reference standards for foreground appearance adjustment, image harmonization i... read more 

The timely detection of impending seizures can offer physicians a critical window of opportunity to implement interventions and enable epileptic patients to take preventive and coping measures promptly. Traditional seizure prediction research has pri... read more 

With the proliferation of multimodal data in real-world applications, Chinese Named Entity Recognition (CNER) has extended beyond textual inputs to incorporate speech and speech-text multimodal scenarios. However, existing methods often treat text-ba... read more 

Multivariate time series (MTS) forecasting is essential for process monitoring and decision optimization in numerous real-world domains. Existing deep models need to be trained from scratch on abundant data, resulting in limited generalization. Inspi... read more