Cultural Competence

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within the...

Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks.

Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive node roles, hindering the acquisition of effective link representations. Subgraph-based methods have been introduced as solutions but often ignore share...

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement.

For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best qua...

Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework.

Algorithmic bias has emerged as a critical challenge in the age of responsible production of artificial intelligence (AI). This paper reviews recent r...

Evaluating generative AI responses to real-world drug-related questions.

Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. Thi...

Systematic review of machine learning models in predicting the risk of bleed/grade of esophageal varices in patients with liver cirrhosis: A comprehensive methodological analysis.

Esophageal varices (EV) in liver cirrhosis carry high mortality risks. Traditional endoscopy, which is costly and subjective, prompts a shift towards ...

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform.

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby impro...

Deep learning bias correction of GEMS tropospheric NO: A comparative validation of NO from GEMS and TROPOMI using Pandora observations.

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This re...

Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.

While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with...

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance.

There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidanc...

The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review.

Prosthodontics is a dental subspecialty that includes the preparation of dental prosthetics for missing or damaged teeth. It increasingly uses compute...

Customer churn modeling in telecommunication using a novel multi-objective evolutionary clustering-based ensemble learning.

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn predict...

Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility.

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with ...

Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection.

Source-free domain adaptation (SFDA) aims to adapt a lightweight pretrained source model to unlabeled new domains without the original labeled source ...

Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news.

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Scie...

Soft Robotics: A Route to Equality, Diversity, and Inclusivity in Robotics.

Robotics is entering our daily lives. The discipline is increasingly crucial in fields such as agriculture, medicine, and rescue operations, impacting...

Evaluating initial usability of a hand augmentation device across a large and diverse sample.

The advancement of motor augmentation and the broader domain of human-machine interaction rely on a seamless integration with users' physical and cogn...

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. Th...

Adaptively identify and refine ill-posed regions for accurate stereo matching.

Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such ...