Latest AI and machine learning research in health policy for healthcare professionals.
In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning (ML) model's application in the medical domain is one of the most compelling and c...
Natural disasters are increasing in frequency and severity, causing hundreds of billions of dollars in damage annually and posing growing threats to infrastructure and human livelihoods. Accurate data on roofing materials is critical for modeling building vulnerability to natural hazards such as earthquakes, floods, wildfires, and hurricanes, yet such data remain unavailable. To address this gap...
Transitional care may play a vital role in the sustainability of Europe's future healthcare system, offering solutions for relocating patient care fro...
Groundwater drought, a prolonged period of abnormally low groundwater levels, poses a significant threat to the environment, society, and economy. Dro...
Regulatory affairs, which sits at the intersection of medicine and law, can benefit significantly from AI-enabled automation. Classification task is...
Advances in markerless motion capture are expanding access to biomechanical movement analysis, making it feasible to obtain high-quality movement da...
Understanding temporal dynamics in clinical narratives is essential for modeling patient trajectories, yet large-scale temporally annotated resource...
Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) ...
The rapid progress in diffusion-based text-to-image (T2I) generation has created an urgent need for interpretable automatic evaluation methods that ...
Recent years have seen rapid development of artificial intelligence (AI) technology revolutionizing the healthcare industry by a tremendous measure, e...
Over 2.6 billion people remain without access to the Internet in 2025. This phenomenon is especially pronounced in developing regions, where cost an...
When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropr...
While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges...
Conformal prediction (CP) provides sets of candidate classes with a guaranteed probability of containing the true class. However, it typically relie...
Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness. We focus on a common form of bias: when two reference ...
"Fake news" is an old problem. In recent years, however, increasing usage of social media as a source of information, the spread of unverified medic...
With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This re...
Imitation learning often assumes that demonstrations are close to optimal according to some fixed, but unknown, cost function. However, according to...
DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) thr...
This paper introduces a novel semantics-aware inspection planning policy derived through deep reinforcement learning. Reflecting the fact that withi...