Latest AI and machine learning research in surveys for healthcare professionals.
INTRODUCTION: This systematic review evaluates the stage-specific diagnostic accuracy of artificial intelligence (AI) models for caries detection and compares their performance with human examiners. METHODS: Following PRISMA 2020 guidelines, four databases (PubMed/Scopus/Embase/ Web of Science) were searched up to October 2025. Nineteen studies using bitewing radiographs and reporting at least one...
BACKGROUND: Although large language models (LLMs) show potential for patient education, their accuracy, usability, and comprehensibility lack validation in high-risk pediatric anesthesia. Rigorous evaluation is therefore essential prior to widespread clinical use in perioperative parental anesthesia education. OBJECTIVE: This study aims to evaluate the accuracy, reliability, and readability of res...
Understanding the causes, consequences, and solutions to global pollinator decline will require more extensive and intensive monitoring programs. Howe...
The rise in importance of narrative intelligence systems that are inspired by artificial intelligence (AI) is increasing in terms of their ability to ...
PURPOSE: The integration of large-language models into medical education assessment holds transformative potential, yet rigorous evaluation of their c...
INTRODUCTION: Conventional risk scores like EuroSCORE II and Society of Thoracic Surgeons models, derived from logistic regression, may not fully repr...
INTRODUCTION: Contrast-enhanced CT is central to oncological imaging, yet no official guidelines exist for contrast injection protocols. As a result, ...
Artificial intelligence (AI) is transforming segmentation tasks in radiotherapy, but model reliability remains a critical concern, particularly for tu...
The measurement performance of the Short-Form Brief Pain Inventory (BPI-SF) across IASP-recognized pain mechanisms remains unclear. We evaluated the r...
Despite high performance of deep learning in medical imaging applications, the critical lack of validated computational metrics for explainable AI (XA...
BACKGROUND AND OBJECTIVES: In the absence of biomarkers, the true biological footprint of migraine remains incompletely understood. It could perhaps b...
BACKGROUND: Although artificial intelligence (AI) is increasingly introduced into medical education, little is known about the psychological mechanism...
PURPOSE: Health-related quality-of-life (HRQOL) assessment provides insight into patient's subjective experiences that complement traditional clinical...
OBJECTIVES: Predictive models are increasingly used to support the clinical management of dengue, but their performance varies widely across settings....
BACKGROUND: The COVID-19 pandemic demonstrated the potential role of digital health tools in enhancing pandemic preparedness and response. These tools...
Despite recent progress in deep leaning for medical image analysis, there are still issues of reliability, interpretability, and uncertainty estimatio...
BACKGROUND: Large language models (LLMs) can generate structured educational content at scale, yet their role in postgraduate radiology training remai...
With the rapid integration of generative artificial intelligence into programming education, concerns have emerged regarding university students' pote...
BACKGROUND: Most patients in Britain undergoing medical abortion under 10 weeks' gestation manage the entire process at home, with access to clinical ...
BackgroundEthical sensitivity is a foundational competency that enables nursing interns to recognize and respond to ethical issues in clinical practic...