Latest AI and machine learning research in health policy for healthcare professionals.
We develop a cost-efficient neurosymbolic agent to address challenging multi-turn image editing tasks such as "Detect the bench in the image while recoloring it to pink. Also, remove the cat for a clearer view and recolor the wall to yellow.'' It combines the fast, high-level subtask planning by large language models (LLMs) with the slow, accurate, tool-use, and local A$^*$ search per subtask to...
We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) e...
Diffusion Policy (DP) enables robots to learn complex behaviors by imitating expert demonstrations through action diffusion. However, in practical a...
Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfo...
Rapid and reliable vascular access is critical in trauma and critical care. Central vascular catheterization enables high-volume resuscitation, hemo...
In the field of image fusion, promising progress has been made by modeling data from different modalities as linear subspaces. However, in practic...
The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation mo...
Digital health interventions offer promise for scalable and accessible health care, but access is still limited by some participatory challenges, espe...
The integration of artificial intelligence (AI) into point-of-care testing (POCT) represents a transformative leap in modern healthcare, addressing cr...
Neural networks excel as function approximators, but their complexity often obscures the nature of the functions they learn. In this work, we propos...
Effective human-AI decision-making balances three key factors: the \textit{correctness} of predictions, the \textit{cost} of knowledge and reasoning...
Effective human-AI decision-making balances three key factors: the \textit{correctness} of predictions, the \textit{cost} of knowledge and reasoning...
We present SLICK, a novel framework for precise and robust car damage segmentation that leverages structural priors and domain knowledge to tackle r...
We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility...
Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools' pose alignment. To access...
Machine Unlearning (MU) aims to update Machine Learning (ML) models following requests to remove training samples and their influences on a trained ...
Digital terrorism is a major cause of securing patient/healthcare providers data and information. Sensitive topics that may have an impact on a pati...
A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-e...
The development lifecycle of generative AI systems requires continual evaluation, data acquisition, and annotation, which is costly in both resource...
Air pollution has emerged as a major public health challenge in megacities. Numerical simulations and single-site machine learning approaches have b...