Latest AI and machine learning research in cultural competence for healthcare professionals.
BACKGROUND: Clinicians are the interface between artificial intelligence (AI) applications and patient care. To maximize benefits and minimize risks of AI, clinicians must be "AI-ready"-that is, willing and able to understand, evaluate, and appropriately use AI tools in practice. Prior literature suggests that clinicians lack fundamental competencies in the use of AI. These gaps could be especiall...
In embodied artificial intelligence (AI), evolutionary search enables adaptation in complex and uncertain environments but relies on massive stochastic sampling, which in hardware is typically generated using complementary metal-oxide-semiconductor pseudorandom number generators with significant power and area costs. In this paper, we present a 256-magnetic-tunnel-junction (MTJ)-based probabilisti...
Global economic shocks such as the 2008 financial crisis or recent trade escalations between the United States and China have exposed the complexity o...
BACKGROUND: Proteins regulate diverse biological processes through interactions with other molecules, including RNAs. RNA-binding proteins (RBPs) are ...
Cancer remains one of the leading causes of death worldwide and continues to pose a serious public health challenge. The limited success of many curre...
BACKGROUND: Oral medications are commonly used in the treatment of breast cancer (BC), despite high rates of nonadherence. As adherence is fundamental...
BACKGROUND: Multimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in En...
This study presents Temporal Convolutional Network-Bidirectional Gated Recurrent Unit-Multi Head Attention (TCN-BiGRU-MHA) hybrid deep learning model ...
Dental age estimation plays a critical role in clinical and forensic applications. Because teeth are highly resistant to environmental degradation, de...
BACKGROUND: Artificial intelligence (AI) has emerged as a transformative tool in oral oncology, enabling earlier detection of oral potentially maligna...
Equity, diversity, and inclusion (EDI) are fundamental to achieving fairness and representation in radiological research and practice. This review aim...
BACKGROUND: Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been...
Combination therapy is an essential strategy for treating complex diseases. However, unintended drug-drug interactions (DDIs) can compromise therapeut...
BACKGROUND: Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning to...
PURPOSE: Convolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi-b DW images wit...
Brain age is a valuable neuroimaging-based biomarker for assessing brain health, typically estimated using machine learning (ML) models. However, ML a...
Non-saponin constituents of Panax species, including amino acids, sugars, and nucleosides, have attracted increasing attention due to their nutritiona...
BACKGROUND: Physicians routinely document specifics of patient encounters in clinic visit notes, a critical but potentially time-consuming task. Ambie...
BACKGROUND: Reliable predictive modeling in high-dimensional biomedical data requires a balance between accuracy, interpretability, and computational ...
Cell migration is a key biological process underlying wound healing, tissue development, and cancer metastasis, yet calibrating mathematical models of...