Latest AI and machine learning research in atopy for healthcare professionals.
BACKGROUND: Integrating Artificial Intelligence (AI) into public health education represents a pivotal advancement in medical knowledge dissemination, particularly for chronic diseases such as asthma. This study assesses the accuracy and comprehensiveness of ChatGPT, a conversational AI model, in providing asthma-related information.
INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.
BACKGROUND: Fluticasone propionate is a synthetic trifluoro-substituted glucocorticoid, a highly selective glucocorticoid receptor agonist. Fluticason...
CD4 T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4 T cell-r...
UNLABELLED: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous dis...
BACKGROUND: Evidence suggests a strong association between vitamin D status and asthma, with individuals exhibiting vitamin D deficiency demonstrating...
Primary human mast cells (MC) obtained through culturing of blood-derived MC progenitors are the preferred model for the study of MRGPRX2- IgE-media...
The milk ladder (ML) approach, which is the gradual reintroduction of the milk allergen from the least allergenic forms to the most allergenic forms i...
Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmon...
INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis ar...
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exi...
Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship betwe...
BACKGROUND: Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and ...
While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitiv...
PURPOSE: This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-l...
This short review illustrates, using two recent studies, the potential and challenges of using machine learning methods to identify phenotypes of whee...
The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, foc...
Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classif...
Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethy...
This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to in...