Artificial Intelligence Medical Compendium

Magnetically guided drug delivery (MGDD) employs magnetic forces acting on magnetically responsive drug delivery systems (DDS) to direct therapeutic agents toward diseased regions, thereby enhancing local drug accumulation while minimising systemic s... read more 

Biomass pyrolysis is a promising thermochemical pathway for producing renewable fuels and chemicals, yet its optimization remains challenging due to the complex interactions among feedstock properties, operating conditions, and reactor configurations... read more 

BACKGROUND: Curative-intent radiotherapy (RT) or chemoradiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC) frequently leads to mucositis, dysphagia, and odynophagia, resulting in nutritional compromise, weight loss, and the need for f... read more 

BACKGROUND: Artificial intelligence (AI) has emerged as one of the most transformative tools for developing clinical decision-support systems in veterinary medicine. Despite its growing use, its full potential remains underutilized in equine medicine... read more 

Predicting drug-target interactions is essential for virtual drug screening. While many models predict the binding affinity between small molecules and proteins, they often overemphasize molecular features while overlooking important protein characte... read more 

Pharmacogenomics investigates how genetic variation influences individual responses to drug therapy and aims to optimize treatment outcomes through personalized approaches. Bioinformatics plays a foundational role in this field, enabling the processi... read more 

OBJECTIVE: Despite growing recognition of pandemic preparedness gaps in low- and lower-middle-income countries (LLMICs), empirical evidence on facility-level capacity remains limited. This study assesses the capacity of health facilities in LLMICs to... read more 

BACKGROUND: Emotion recognition is increasingly essential for diagnosing mental disorders like depression and anxiety. Electroencephalography (EEG) is widely adopted for this purpose due to its high temporal resolution and non-invasive nature. Howeve... read more 

OBJECTIVE: This systematic literature review explores how temporal and time-related dimensions are incorporated into Machine Learning (ML) models used in Clinical Decision Support Systems (CDSS) for cancer. The study examines current applications, id... read more 

Graph partitioning aims to partition nodes into different local groups of equal size. Recently, graph neural networks (GNNs) have been widely used to solve graph partitioning problems. They generally follow a neighbor aggregation scheme to learn stru... read more