Latest AI and machine learning research in health policy for healthcare professionals.
This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory...
ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or cancelled) using a large national electronic health record (EHR) repository in the United States. DesignRetrospective observational study using machine learning and statistical modeling. SettingA U.S. national electronic health record repository (Cerner Real World Database) covering healthcare encou...
Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habit...
Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies...
Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels o...
Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habit...
Classifier-free Guidance (CFG) lets practitioners trade-off fidelity against diversity in Diffusion Models (DMs). The practicality of CFG is however h...
Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencie...
Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning bench...
Reinforcement-Learning-based post-training has recently emerged as a promising paradigm for aligning text-to-image diffusion models with human prefere...
World action models (WAMs) have emerged as a promising direction for robot policy learning, as they can leverage powerful video backbones to model the...
Deploying large multimodal language models at scale is constrained by token-based inference costs, yet the cost-performance behavior of visual prompti...
Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and world models, unifying their reasoning and instruct...
Artificial intelligence (AI) systems accelerate medical workflows and improve diagnostic accuracy in healthcare, serving as second-opinion systems. Ho...
Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk as...
Abstract Background: Cardiovascular disease is the leading cause of mortality in Nigeria and across sub-Saharan Africa, with rising incidence attribut...
Large Language Models achieve impressive accuracy on medical benchmarks that present clinical information as complete vignettes, but their behavior un...
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high...
Self-forcing video generation extends a short-horizon video model to longer rollouts by repeatedly feeding generated content back in as context. This ...
Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing frameworks ...