Latest AI and machine learning research in cultural competence for healthcare professionals.
Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitigation methods focus on a small set of predefined biases, limiting their applicability in visual datasets where multiple, possibly unknown biases exist. To address this limitation, we introduce MAVias, an open-set bias mitigation approach leveraging fo...
In the rapidly evolving field of Large Language Models (LLMs), ensuring safety is a crucial and widely discussed topic. However, existing works often overlook the geo-diversity of cultural and legal standards across the world. To demonstrate the challenges posed by geo-diverse safety standards, we introduce SafeWorld, a novel benchmark specifically designed to evaluate LLMs' ability to generate ...
Predictive machine learning (ML) models are computational innovations that can enhance medical decision-making, including aiding in determining opti...
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal infere...
The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment...
Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneury...
In medical image analysis, model predictions can be affected by sensitive attributes, such as race and gender, leading to fairness concerns and pote...
Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce ha...
With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements...
Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting their flexibility across diver...
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and ann...
Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this appro...
To tackle the challenges of large language model performance in natural language to SQL tasks, we introduce XiYan-SQL, an innovative framework that ...
Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in Engl...
Text-conditioned generation models are commonly evaluated based on the quality of the generated data and its alignment with the input text prompt. O...
Federated Learning (FL) has emerged as a transformative approach in healthcare, enabling collaborative model training across decentralized data sour...
OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance ...
Human image datasets used to develop and evaluate technology should represent the diversity of human phenotypes, including skin tone. Datasets that ...
In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learn...
Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology, yet our understanding of how bias manifests in the p...