Latest AI and machine learning research in surveys for healthcare professionals.
The integration of vision-language models into robotic systems constitutes a significant advancement in enabling machines to interact with their surroundings in a more intuitive manner. While VLMs offer rich multimodal reasoning, existing approaches lack user-specific adaptability, often relying on generic interaction paradigms that fail to account for individual behavioral, contextual, or socio...
Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained contexts. However, no studies so far have focused on gender biases conveyed by LLMs' responses to generic instructions, especially with regard to masculine generics (MG). MG are a linguistic feature found in many gender-marked languages, denoting the ...
Studies of human cognition often rely on brief, controlled tasks emphasizing group-level effects but poorly capturing individual variability. A suit...
We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address...
Graph Convolutional Networks (GCNs) have emerged as a promising approach to machine learning on Electronic Health Records (EHRs). By constructing a ...
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affecte...
The growing interest in eXplainable Artificial Intelligence (XAI) has prompted research into models with built-in interpretability, the most promine...
The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, phot...
Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial int...
Large Language Models (LLMs) struggle with hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented ...
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. ...
Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of da...
Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of...
Diffusion Models~(DMs) have emerged as the dominant approach in Generative Artificial Intelligence (GenAI), owing to their remarkable performance in...
Recent advances in large language models (LLMs) have shown promising improvements, often surpassing existing methods across a wide range of downstre...
Bias and inequity in palliative care disproportionately affect marginalised groups. Large language models (LLMs), such as GPT-4o, hold potential to ...
Wide coverage and high-precision rural household wealth data is an important support for the effective connection between the national macro rural r...
While recent work has found that vision-language models trained under the Contrastive Language Image Pre-training (CLIP) framework contain intrinsic...
Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual ques...
Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic b...