Latest AI and machine learning research in surveys for healthcare professionals.
The latest research on Large Language Models (LLMs) has demonstrated significant advancement in the field of Natural Language Processing (NLP). However, despite this progress, there is still a lack of reliability in these models. This is due to the stochastic architecture of LLMs, which presents a challenge for users attempting to ascertain the reliability of a model's response. These responses ...
Fault diagnosis of mechanical equipment involves data collection, feature extraction, and pattern recognition but is often hindered by the imbalanced nature of industrial data, introducing significant uncertainty and reducing diagnostic reliability. To address these challenges, this study proposes the Uncertainty-Aware Bayesian Meta-Learning Framework (UBMF), which integrates four key modules: d...
The growing homelessness crisis in the U.S. presents complex social, economic, and public health challenges, straining shelters, healthcare, and soc...
Training-free video large language models (LLMs) leverage pretrained Image LLMs to process video content without the need for further training. A ke...
Recommendation systems have found extensive applications across diverse domains. However, the training data available typically comprises implicit f...
It is clear that artificial intelligence-based chatbots will be popular applications in the field of healthcare in the near future. It is known that m...
Integration of Vision-Language Models (VLMs) in surgical intelligence is hindered by hallucinations, domain knowledge gaps, and limited understandin...
Neural architectures tend to fit their data with relatively simple functions. This "simplicity bias" is widely regarded as key to their success. Thi...
Data-driven AI is establishing itself at the center of evidence-based medicine. However, reports of shortcomings and unexpected behavior are growing...
Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims ...
The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Ad...
Transformers, particularly Vision Transformers (ViTs), have achieved state-of-the-art performance in large-scale image classification. However, they...
In recent years, zoological institutions have made significant strides to reimagine ex situ animal habitats, moving away from traditional single-spe...
Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopatho...
Integrating BD-RIS into wireless communications systems has attracted significant interest due to its transformative potential in enhancing system p...
Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to the...
We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms li...
When we train models on biased ML datasets, they not only learn these biases but can inflate them at test time - a phenomenon called bias amplificat...
This research investigates both explicit and implicit social biases exhibited by Vision-Language Models (VLMs). The key distinction between these bi...
Unsupervised image complexity representation often suffers from bias in positive sample selection and sensitivity to image content. We propose CLICv...