Artificial Intelligence Medical Compendium

The allergenic characteristics of anemophilous pollen differ among taxa, and its atmospheric concentration plays a critical role in influencing allergy risks and health assessments. Based on phenology and artificial intelligence methods, the study co... read more 

Chronic microbial diseases, often driven by biofilm formation, pose a persistent global health burden due to their complex diagnosis, resistance mechanisms, and prolonged disease courses. Conventional diagnostic methods are time-consuming and often i... read more 

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Continual learning (CL) enables models to adapt to and retain knowledge across a sequence of tasks. While recent theoretical studies primarily focus on CL without memory buffers, the theoretical analysis of memory buffer-based continual learning (MCL... read more 

Psychiatry currently faces the following four intersecting challenges: technological disruption through artificial intelligence (AI); the loss of exclusive prescribing authority; escalating systemic constraints within public psychiatry, and market-dr... read more 

Inflammatory bowel disease (IBD) is a chronic disorder that requires long-term follow-up and individualized management. With the rapid development of digital health technologies (DHTs), tools such as telemedicine, smartphone applications, wearable de... read more 

OBJECTIVES: Follistatin-like protein-1 (FSTL-1) is emerging as a myokine linking skeletal and muscle biology. We investigated the relationship between circulating FSTL-1 levels and muscle mass and quality, using an artificial intelligence (AI)-enhanc... read more 

BACKGROUND: Circulatory disturbances in sepsis are heterogeneous but may manifest as a mismatch between peripheral perfusion and tissue oxygenation. Peripheral perfusion index (PI) and central venous oxygen saturation (ScvO 2 ) have been independentl... read more 

OBJECTIVES: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the urgent need for improved diagnostic and predictive methodologies. The several challenges in the complexity and high dimensionality of ge... read more 

OBJECTIVES: Accurate lung cancer prediction from CT scans using advanced deep learning methods is crucial for improving early diagnosis and treatment outcomes, as it harnesses innovative algorithms to enhance the detection and classification of malig... read more