BACKGROUND: Rheumatoid arthritis (RA), particularly seronegative disease, is difficult to diagnose early, which can delay treatment initiation. This study aims to develop a binary DNA methylation (DNAm)-based algorithm to diagnose RA.
OBJECTIVE: The integration of artificial intelligence (AI) into CAD/CAM workflows has revolutionized dental prosthetics manufacturing, yet its morphological trueness compared to manual design remains underexplored.
BACKGROUND: The advancement of digital technologies has brought transformative changes across the healthcare sector, and nursing is no exception. However, existing research has largely overlooked the ethical challenges nursing students face in real-w...
BACKGROUND: It remains unclear whether certain individuals with type 2 diabetes (T2D) derive greater cardiovascular benefit from GLP-1 receptor agonists (GLP-1RAs). Here, we integrate individual-level data from cardiovascular outcome trials (CVOTs) a...
This study investigates how different forms of literacy shape trust in medical AI and its transfer in healthcare contexts. Based on a survey of 1,250 participants, three findings emerge. First, digital literacy and AI literacy exert opposite influenc...
BACKGROUND: Rising osteoporosis prevalence among elderly populations and limitations of current single-factor screening methods necessitate development of comprehensive multi-dimensional risk prediction models.
BACKGROUND: Insomnia is a significant independent risk factor for depression and suicidality. However, these conditions often go undetected, particularly in individuals presenting with sleep complaints. This study aimed to develop and validate machin...
BACKGROUND: The clinical significance and contribution of the lipid profile in atherosclerosis are well established. However, further investigation is needed in stroke patients, particularly regarding apolipoprotein B100 (ApoB100), a novel non-tradit...
BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...
BACKGROUND: Machine learning (ML) models have shown good performance in predicting cardiovascular disease risk. However, the usefulness of ML models has yet to be fully elucidated for sudden cardiac death (SCD) risk using long-term follow-up electron...
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