Latest AI and machine learning research in health policy for healthcare professionals.
Frontier artificial intelligence (AI) models have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging is underrepresented in the public domain due to identifiable facial features within magnetic resonance imaging (MRI) and computed tomography (CT) scans, restricting model performance in clinical medicine. Here we show...
BACKGROUND: Pulmonary tuberculosis (TB) is a chronic infectious disease that burdens patients and public health systems. Limited reach of traditional education and uneven online information may undermine patients' understanding, adherence, and trust. Large language models (LLMs) show promise for TB health education, but systematic evaluation is lacking. OBJECTIVE: To evaluate five large language m...
The integration of artificial intelligence (AI) in healthcare presents significant opportunities to enhance patient care and streamline medical workfl...
PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' rel...
Access to digital health tools is rapidly expanding, but adoption in respiratory care remains fragmented. The ERS CONNECT Repository provides a truste...
Families often report searching the internet for guidance on how best to support children when a significant adult has cancer. This study aimed to ide...
BACKGROUND: Fentanyl overdose deaths are still increasing across the U.S. Even though the crisis is growing, we still do not fully understand which co...
Anatomy education is increasingly challenged by limited access to body donor materials, high infrastructure costs, and unequal availability of advance...
BACKGROUND: Youth-onset diabetes mellitus (DM) is an increasing public health concern, especially among adolescents facing socioeconomic challenges. F...
Explainable AI (XAI) is increasingly used in clinical machine learning, yet quantitative evaluation of explanation quality is often reported inconsist...
Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder worldwide, with projections exceeding 25 million people by 2050. Its burden...
BACKGROUND: Manual chart abstraction from electronic health records is a critical step in clinical outcomes research but is time-intensive and prone t...
Atopic dermatitis (AD) and allergic contact dermatitis (ACD) are common inflammatory skin diseases influenced by environmental factors, but disease-sp...
PURPOSE: To introduce and evaluate OphthoChat, a Health Insurance Portability and Accountability Act-compliant, artificial intelligence (AI)‑powered n...
AIMS: To define rates of diagnostic image acquisition, clinical drivers of image quality and the learning curve for artificial intelligence (AI)-guide...
PURPOSE: To examine the impact of artificial intelligence (AI) on radiologists' workload and economic outcomes by synthesizing current evidence on wor...
As Additive Manufacturing (AM) shifts towards Make-to-Order (MTO) models, synchronizing raw material inventory with machine capacity becomes critical ...
Electronic health records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional ma...
INTRODUCTION AND OBJECTIVE: Artificial intelligence is playing an increasingly important role in healthcare, particularly in diagnostics, clinical dec...
Artificial intelligence (AI) applications bear a great promise for healthcare. If successfully implemented, AI could reduce the workload for healthcar...