Latest AI and machine learning research in health policy for healthcare professionals.
Any tool that can reduce the administrative burden on healthcare providers while preserving safe, accountable and high-quality medical documentation is of immense value both to healthcare institutions and consumers. The key question we need to answer is whether a prospective tool can reduce these burdens while maintaining (and, ideally, elevating) quality documentation standards. The goal of this ...
Economic evaluations of artificial intelligence (AI) in healthcare are expanding rapidly, yet underlying costing methods remains heterogenous, and frequently incomplete for health technology assessment (HTA) and policy decision-making. In our systematic review of 55 studies published between 2010 and 2025, we found that fewer than half of the studies reported explicit costing methods; most pricing...
To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...
Healthcare systems globally are under increasing financial and operational strain due to aging populations, rising expenditures, and workforce shortag...
Evaluating the outputs of generative AI (GenAI) models in healthcare remains a significant bottleneck for the safe and scalable deployment of these to...
Large language models (LLMs) have been investigated for clinical documentation, with concerns about hallucinations and factual errors. Clinician revie...
Large language models (LLMs) are increasingly used in healthcare, but standardized benchmarks fail to capture their validity and safety in real-world ...
We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial...
Sleep disorders, including insomnia and obstructive sleep apnea, affect millions of individuals worldwide but are frequently undetected due to the hig...
Artificial intelligence and automation technologies are displacing millions of workers across industries in developed countries, while many developing...
Understanding temporal dynamics in clinical narratives is essential for modeling patient trajectories, yet large-scale temporally annotated resources ...
Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general populat...
Unintended pregnancy remains common in high income countries and has been linked to poorer maternal and neonatal outcomes. Whether pregnancy intention...
Neighborhood physical, social, and service environments are increasingly recognized as important contextual factors related to cognitive health; howev...
Timely linkage to HIV prevention and treatment services following HIV self-testing (HIVST) remains a challenge in many countries. While HIVST offers p...
The clinical promise of Large Language Models (LLMs) is often unrealized due to pro-hibitive computational costs. These costs create barriers not only...
Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, especially during abstract screening. This study evalua...
Traditional LDL-C testing barriers—mandatory 9–12 hour fasting and inperson visits—disproportionately limit access for rural populations (60% of US co...
Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been sh...
We propose a simulator-driven imitation learning framework for sequential decision making in head and neck cancer (HNC) treatment. Our method, Superhu...