Latest AI and machine learning research in cultural competence for healthcare professionals.
Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods make it difficult to understand the reasoning behind their decisions. This lack of transparency can...
Supervised machine learning (ML) is revolutionising healthcare, but the acquisition of reliable labels for signals harvested from medical sensors is usually challenging, manual, and costly. Active learning can assist in establishing labels on-the-fly by querying the user only for the most uncertain -and thus informative- samples. However, current approaches rely on naive data selection algorithms,...
The growing accessibility of large health datasets and AI's ability to analyze them offers significant potential to transform public health and epidem...
In this communication, we demonstrate that the bias observed in domain general training sets with health-related content is not improved in domain spe...
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the divers...
The bias-variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorit...
The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare a...
Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supe...
In the analog-to-digital converter (ADC) test process, the static and dynamic performance parameters are the most important, and the tests for these p...
This work explores the possibility of applying edge machine learning technology in the context of portable medical image diagnostic systems. This was ...
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this...
Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance...
Multimorbidity, having a diagnosis of two or more chronic conditions, increases as people age. It is a predictor used in clinical decision-making, but...
Artificial intelligence (AI)-enhanced interventions show promise for improving the delivery of long-term care (LTC) services for older people. However...
There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin ...
The rotating component is an important part of the modern mechanical equipment, and its health status has a great impact on whether the equipment can ...
As the last decade of human genomics research begins to bear the fruit of advancements in precision medicine, it is important to ensure that genomics'...
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity ...
The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessmen...