Latest AI and machine learning research in us health policy for healthcare professionals.
Intra-tumoural heterogeneity (ITH) reflects spatial variation in tumour biology and is an important determinant of tumour behaviour, prognosis, and treatment response. Radiomics and deep learning have shown promise for tumour classification from multiparametric MRI (mp-MRI), but radiomics relies on handcrafted features, while most deep learning methods use whole-tumour representations or manually ...
Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled noising versions of the rollout samples. This paper shows that the...
Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a...
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc., yet most deployments remain isolated point solutions lock...
Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the "randomness" they consume is realized as deterministic numerical orb...
CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume repr...
Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. Howe...
FeePredict is a three-stage random forest machine learning framework to simul-taneously predict whether Medicare reimbursement rates for specific proc...
Learning the value of environmental stimuli from reward experience allows animals to make advantageous choices. Existing biological accounts of reinfo...
Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of thi...
People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and dev...
Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease ...
The cost of healthcare remains a concern in the United States and may have been influenced by disruptions associated with the COVID-19 pandemic. This ...
Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional l...
Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear ...
Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost o...
Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minim...
Introduction. Systematic reviews are essential for informing health policy and practice. Artificial intelligence (AI) automates the article screening ...
The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance dispa...
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and partic...