Latest AI and machine learning research in health policy for healthcare professionals.
The unintended biases introduced by optimization and machine learning (ML) models are a topic of great interest to medical researchers and professionals. Bias in healthcare decisions can cause patients from vulnerable populations (e.g., racially minoritized, low-income, or living in rural areas) to have lower access to resources and inferior outcomes, thus exacerbating societal unfairness. In this...
We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during an EHR transition. This end-to-end AI system demonstrated high fidelity, scalability, and a projected prevention of up to 6092 colorectal cancer cases and cost savings between 400 - 670 million dollars. The rise of structured data elements in Electron...
Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk st...
Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks....
Despite policy support, inappropriate use of emergency contraception (EC) in Ethiopia contributes to high rates of unintended pregnancy and maternal m...
Post-translational modifications (PTMs) represent a fourth dimension of the genetic code, orchestrated by the Golgi apparatus and central to the biolo...
Large language models are increasingly used to generate patient information in healthcare. However, their ability to communicate complex topics, such ...
The last three years have seen an explosion in published manuscripts analysing open-access health datasets, in many cases presenting misleading or bio...
This study investigates the effectiveness of different large language models (LLMs) for automated biomedical entity annotation in research articles wi...
Diagnostic errors remain a pervasive yet preventable source of patient harm, with resourcelimited healthcare systems in low- and middle-income countri...
Mental disorders pose significant challenges to healthcare systems and have profound social implications. The rapid development of large language mode...
AI agents built on large language models (LLMs) can plan tasks, use external tools, and coordinate with other agents. Unlike standard LLMs, agents can...
The timely detection of ward deterioration—including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis—remains an unmet ...
Malaria remains a persistent public health challenge in Zimbabwe, particularly in rural districts such as Mudzi in Mashonaland East Province, where se...
Disparities in the quality of healthcare persist globally, with poor-quality care contributing significantly to preventable mortality, particularly in...
Tuberculosis (TB) remains a leading global cause of preventable death, with 10.8 million cases and 1.3 million deaths reported in 2023. Current method...
Access to high-quality data provides the foundation for biomedical research. But data access is often limited or challenging due to privacy constraint...
A paper from Goh et al found that a large language model (LLM) working alone outperformed American clinicians assisted by the same LLM in diagnostic r...
The emergence of Large Language Models (LLMs) like ChatGPT presents significant opportunities for healthcare, yet raises concerns about accuracy, espe...
Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...