Latest AI and machine learning research in surveys for healthcare professionals.
The concept of algorithmic fairness is intricate and multifaceted, concerning the equitable treatment of individuals or groups by algorithms in data processing and decision-making. This issue not only impacts trust in algorithms but also influences societal acceptance and reliance on technology. With the growing utilisation of big data and machine learning in predictive algorithms, crime predictio...
Platelet-biased hematopoietic stem cells (PLT-HSCs) play key roles in normal physiology, aging, and blood cancer. However, currently, no markers allow their accurate identification or prospective isolation. We here combine single-mouse hematopoietic stem cell (HSC) gene expression, chromatin accessibility, and surface proteome profiling to identify subtype-specific markers. Using machine learning,...
OBJECTIVE: In intrapartum cardiotocography (CTG), computer and artificial intelligence (AI) analyses are being increasingly used. However, fetal heart...
Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...
AIMS: To understand the current state of nurses' artificial intelligence (AI) literacy. This study employs latent profile analysis to examine the rela...
BACKGROUND: The rapid integration of artificial intelligence (AI) in clinical settings presents unprecedented opportunities, yet it concurrently trigg...
Introduced in 2014 and revised in 2018, the entropic brain hypothesis has accrued a wealth of supportive evidence. The hypothesis states that-along a ...
INTRODUCTION: Traditional program evaluation in medical education often faces challenges with large data volumes and manual analysis, which can delay ...
BackgroundAsynchronous telemedicine may support home-based pediatric palliative care (PPC) by improving access to professional guidance and reducing c...
BACKGROUND: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated pote...
Autonomous artificial intelligence (AI) systems for retinal image interpretation are being deployed in routine clinical practice, fundamentally alteri...
Emergency department overcrowding places sustained pressure on triage workflows and patient prioritization. Artificial intelligence (AI)-augmented tri...
There has been emerging empirical evidence supporting the role of artificial intelligence (AI) knowledge in public support for AI. These findings stan...
INTRODUCTION/OBJECTIVES: General-purpose large language models (LLMs) have substantial limitations, including fabricated references and inconsistent c...
SIGNIFICANCE: Pediatric pressure injuries (PIs) are a distinct and preventable clinical challenge, yet risk prediction models tailored to children rem...
BACKGROUND: Spirometry remains the gold standard for assessing pulmonary function. Deep learning models have demonstrated potential for estimating mea...
BACKGROUND: With the rapid development of artificial intelligence (AI) technology, its application across various industries, particularly in healthca...
BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to spec...
BACKGROUND AND OBJECTIVES: Rapid advancements in artificial intelligence (AI) have facilitated the widespread integration of AI chatbots into everyday...
The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved saf...