Latest AI and machine learning research in surveys for healthcare professionals.
Conventional performance metrics in clinical decision support systems, such as accuracy or sensitivity, fail to reflect the reliability of individual predictions-an essential concern for clinicians operating in high-stakes environments. We introduce a calibration-informed framework featuring two novel metrics: the Local Predictive Value (LPV) and the Credible Predictive Value (CPV). LPV estimates ...
AIM: To synthesize literature on algorithmic bias and transparency in artificial intelligence tools used in nursing education and identify common applications, bias types, transparency challenges and mitigation strategies. BACKGROUND: Artificial intelligence is increasingly embedded in nursing education through tutoring systems, virtual simulations, predictive models, chatbots and automated gradin...
BACKGROUND: Artificial intelligence (AI) and machine-learning technology are on the rise, including ChatGPT and Google's Gemini, previously known as B...
INTRODUCTION AND AIMS: To develop a multimodal deep learning model for dental caries screening in children by integrating intraoral photographs and qu...
BACKGROUND: Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinic...
Rainfall and temperature are key climate determinants of malaria incidence; yet their causal exposure-response curves on malaria incidence across the ...
Acquired Brain Injury (ABI) refers to any post-birth damage to the brain, commonly resulting from traumatic events (traumatic brain injury) or non-tra...
BACKGROUND: Prediction models for child maltreatment risk are increasingly used to support decisions in child protection, yet concerns remain about me...
Artificial intelligence (AI) is increasingly being used in oncology to support early diagnosis and develop personalized treatment plans. However, its ...
OBJECTIVE: This study aimed to evaluate the awareness levels of actively practicing dentists in Türkiye regarding artificial intelligence (AI)-related...
BACKGROUND: Adolescents are particularly vulnerable to mental disorders, with over 75% of lifetime cases emerging before the age of 25 years. Yet most...
BACKGROUND: Artificial intelligence (AI) is being rapidly integrated into oncologic care, yet little is known about how patients perceive these applic...
OBJECTIVES: Despite growing interest in artificial intelligence (AI) and machine learning (ML), many laboratory professionals lack experience with dev...
BACKGROUND: While artificial intelligence (AI) holds significant promise for health care, excessive trust in these tools may unintentionally delay pat...
BACKGROUND: Mobile health (mHealth), leveraging mobile devices for health measurement and promotion, is rapidly growing. Smartphone cameras can perfor...
The low quantitative accuracy of conventional small noncoding RNA sequencing (sncRNA-seq) methods due to extensive ligation bias commonly limits funct...
The integration of artificial intelligence (AI) into healthcare presents transformative opportunities, but patient perspectives, particularly from dig...
BACKGROUND: Pulmonary vein (PV) isolation is a well-established treatment for atrial fibrillation (AF), however, strategies for patients with recurren...
BACKGROUND: Existing methods for estimating GFR in people with diabetes have shown inaccuracies when compared to mGFR measurements. We developed and v...
Artificial intelligence (AI) decision support tools (DSTs) are increasingly used across clinical settings to improve efficiency and support decision-m...