Artificial Intelligence Medical Compendium

BACKGROUND: Artificial intelligence literacy is essential for nursing students to become competent in navigating contemporary healthcare complexities and to ensure safe patient care. This literacy is needed urgently due to the rapid integration of ar... read more 

BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) remains a major public health challenge in South Asia, which bears a disproportionate global burden. Comprehensive, longitudinal analyses of MDR-TB mortality trends, stratified by country and sex,... read more 

Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning architectures. Digital image datasets serve as the main input for these architectures, enabling the analys... read more 

BACKGROUND: N-phenyl-N'-(1,3-dimethylbutyl)-p-phenylenediamine-quinone (6PPD-Q), an emerging pollutant, is a highly toxic chemical derived from tires, which have possible adverse effects on human health via the food chain. Despite the widespread occu... read more 

In this paper, we propose Kernelized Linear Principal Component Discriminant Analysis (KLPCDA), a structured and unified framework for discriminant analysis that overcomes the fragmentation in existing multi-stage approaches such as PCA+LDA or KPCA+G... read more 

OBJECTIVES: Electronic health records (EHRs) rarely capture dietary detail, limiting diet-disease research. We aimed to develop machine learning (ML) computable phenotypes to identify high-fat diet (HFD) using variables typically available in EHRs. M... read more 

OBJECTIVES: The surge in publications increases screening time required to maintain high-quality literature reviews. One of the most time-consuming phases is title and abstract screening. Machine learning tools have semi-automated this process for sy... read more 

Pronoun resolution represents a fundamental language comprehension process that varies in cognitive complexity. Prior studies have identified behavioral and neural differences in pronoun processing, but existing models struggle to address background ... read more 

Inspired by fuzzy inference systems and neural networks, this paper presents the design of a memristive fuzzy neural network (M-FNN) with applications to classification tasks, implemented in a computing-in-memory (CIM) architecture. Specifically, a m... read more 

Self-supervised learning autonomously extracts features from unlabeled data, supporting downstream segmentation tasks with limited annotations. However, variations in devices, imaging parameters, and other factors lead to differences in the distribut... read more