Cultural Competence

Artificial Intelligence Bias in Health Care: Web-Based Survey.

BACKGROUND: Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers.

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical...

Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection.

Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional traini...

Comparing ChatGPT’s ability to rate the degree of stereotypes and the consistency of stereotype attribution with those of medical students in New Zealand in developing a similarity rating test: a methodological study.

Learning about one’s implicit bias is crucial for improving one’s cultural competency and thereby reducing health inequity. To evaluate bias among med...

HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation.

High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations ar...

[The Perspectives of Stakeholders on Users of Care Robotics at the Intersection of Age and Gender - Results of a Qualitative Analysis].

Having to face the challenges posed by a shortage of skilled care workers and an increasing number of older people in need of care, policy makers and ...

AI and machine learning ethics, law, diversity, and global impact.

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective he...

Research Participant Selection Bias in the Workshop Using Socially Assistive Robots for Older Adults and Its Effect on Population Representativeness.

Every research participant has their own personality characteristics. For example, older adults assisted by socially assistive robots (SAR) may have t...

Learning in an era of uncertainty in Singapore: diversity, lifelong learning, inspiration and paradigm shift.

This is an era of uncertainty, during which adaptability is a key capability to survival and future success. What has Singapore done to develop an edu...

Why the power of diversity does not always produce better groups and societies.

Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better grou...

2D medical image synthesis using transformer-based denoising diffusion probabilistic model.

. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed fo...

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesio...

Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance.

Background Automation bias (the propensity for humans to favor suggestions from automated decision-making systems) is a known source of error in human...

Survey and Evaluation of Hypertension Machine Learning Research.

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in di...

Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective.

Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential t...

Artificial Intelligence Technologies in College English Translation Teaching.

This paper explores the practical prospects for using artificial intelligence technologies in professional English-speaking translator education. At t...

Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.

Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented o...

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.

Developmental dysplasia of the hip (DDH) is a cluster of hip development disorders and one of the most common hip diseases in infants. Hip radiography...

Open Data and transparency in artificial intelligence and machine learning: A new era of research.

Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, includ...

How cultural framing can bias our beliefs about robots and artificial intelligence.

Clark and Fischer argue that humans treat social artifacts as depictions. In contrast, theories of distributed cognition suggest that there is no clea...