BACKGROUND: Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter ...
BACKGROUND: Large language models (LLMs) are transforming how data is used, including within the health care sector. However, frameworks including the Unified Theory of Acceptance and Use of Technology highlight the importance of understanding the fa...
IMPORTANCE: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of bot...
Polycystic ovary syndrome (PCOS) is a heterogeneous multifactorial endocrine disorder that affects one in five women around the globe. The pathology suggests a strong polygenic and epigenetic correlation, along with hormonal and metabolic dysfunction...
BACKGROUND: To compare liver image quality and lesion detection using an AI-augmented T1-weighted sequence on hepatobiliary-phase gadoxetate-enhanced magnetic resonance imaging (MRI).
This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic c...
This study aimed to compare Generations X, Y, and Z in terms of anxiety and readiness levels regarding artificial intelligence and investigate the relationship between anxiety and readiness levels regarding artificial intelligence in midwives across ...
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...
PURPOSE: Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model.
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...