Latest AI and machine learning research in cultural competence for healthcare professionals.
This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoretical guarantees. GANs are typically learned through a minimax game between a generator and a discriminator, which is known to be empirically unstable. Previous learning paradigms have encountered mode collapse issues without a theoretical solution. To...
Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to the need for high-quality data and expert annotators. Synthetic data generation offers an alternative, though pre-trained models may not capture the syntactic diversity of clinical notes. We propose an embedding-driven approach that uses diversity sa...
With the wide application of new technologies such as large language models and generative artificial intelligence (AI) in the health care sector, art...
We propose a new continuous video modeling framework based on implicit neural representations (INRs) called ActINR. At the core of our approach is t...
Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health thr...
Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and tea...
Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widesprea...
The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networ...
This research-in-progress paper presents a new project management framework that utilises GenAI technology. The framework is designed to address the...
A recent report from the World Meteorological Organization (WMO) highlights that water-related disasters have caused the highest human losses among ...
The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-f...
This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupa...
The use of AI in healthcare has the potential to improve patient care, optimize clinical workflows, and enhance decision-making. However, bias, data...
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper...
The generalization problem is broadly recognized as a critical challenge in detecting deepfakes. Most previous work believes that the generalization...
In this work, we build upon the offline reinforcement learning algorithm TD7, which incorporates State-Action Learned Embeddings (SALE) and a priori...
Recent years have witnessed significant advancements in text-guided style transfer, primarily attributed to innovations in diffusion models. These m...
Background: Late Gadolinium Enhancement (LGE) imaging is the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV...
Accurate prediction of the temporal dynamics of biological systems is crucial for informing timely and effective interventions, e.g., in ecological or...
Compiling and characterising the diversity of bacterial pathogens of humans is a critical challenge to tackle infection risk, especially in the contex...