Latest AI and machine learning research in health policy for healthcare professionals.
Emerging uplink-dominant 6G use cases, such as cooperative vehicular streaming, require efficient transmission of high-volume visual data over limited wireless resources. While semantic communications can reduce traffic by prioritizing task-relevant content, most existing approaches treat users independently and therefore overlook spatial redundancy among nearby devices' observations. This paper p...
BackgroundGaps in care (GIC) among patients with congenital heart disease (CHD) are associated with adverse outcomes, yet the specific social and healthcare-related factors contributing to GIC and the clinical consequences of delayed re-engagement in care remain poorly characterized. Large electronic medical record datasets often cannot distinguish true GIC from clinically appropriate care pattern...
Blind image quality assessment (BIQA) is commonly built on two basic learning paradigms: regression and ranking. Regression calibrates absolute scores...
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement proc...
Blind image quality assessment (BIQA) is commonly built on two basic learning paradigms: regression and ranking. Regression calibrates absolute scores...
Large language models (LLMs) are increasingly used for automated quality control (QC) of radiology reports. However, the reliability of LLMs on report...
Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinica...
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD)...
Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly a...
Introduction Stillbirth prevention requires reliable detection of potential causes for timely interventions. Currently, there is no effective screenin...
Camera-based monitoring systems are increasingly adopted in healthcare settings for the continuous assessment of patient movement and activities. Howe...
With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although pr...
Inference efficiency is typically pursued by shrinking the model: distillation, pruning, quantization, and sparse routing each lower per-token cost wh...
Background: Manual review of 30-day hospital readmissions can identify actionable quality and safety problems, but it is labor-intensive. We developed...
Biological databases store curated knowledge that researchers traditionally access through web interfaces or APIs. To move beyond casual browsing requ...
Image quality control is vital for a wide range of downstream applications. Deep learning-based image quality assessment methods typically train class...
Vision-language models (VLMs) are powerful for chart question answering, but invoking a VLM for every query can be unnecessarily expensive when many q...
Evidence-based medicine demands clinical answers that are not only fluent and medically plausible, but also anchored in traceable evidence, tailored t...
Memory benchmarks for LLM agents largely assume single-user settings, leaving shared assistants for hospitals, workplaces, campuses, and households un...
Clinical prostate multi-parametric MRI relies heavily on high-quality diffusion-weighted imaging (DWI), yet reading DWI is frequently compromised by g...