Artificial intelligence in the diagnosis of pediatric allergic diseases

Abstract

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

“…AI is defined as the ability of advanced computing machines to mimic human intelligence and behavior, learn from data and perform tasks. [195,196,197] As a subset of AI, machine learning allows the machine to automatically learn from experience to improve the prediction accuracy. [197] These approaches can quickly merge all the information from medical data, text, images, bioinformatics and provide multiple perspectives of health status assessments to help clinicians come to accurate diagnosis.…”

“…[195,196,197] As a subset of AI, machine learning allows the machine to automatically learn from experience to improve the prediction accuracy. [197] These approaches can quickly merge all the information from medical data, text, images, bioinformatics and provide multiple perspectives of health status assessments to help clinicians come to accurate diagnosis. [195] It not only improves the efficiency of healthcare professionals, but also reduces healthcare costs.…”

“…[196] Enormous progress has been made in machine learning algorithms, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), Principal Components Analysis (PCA), and Hierarchical Cluster Analysis (HCA). [195,197] These machine learning algorithms can be used to analyze healthcare data to improve the efficiency of disease diagnosis and the quality of treatment. Additionally, they can be used to explore the process of disease development, predict disease risks, or to assist clinical diagnosis and precise targeting treatments.…”

Comments on vitamin D in asthma, milk allergy diagnosis, and stem cell transplantation in chronic granulomatous disease

“…However, for most of us, it still remains rather obscure how it influences our decisions in clinical care as well as in research. By reading the opening article by Ferrante and colleagues, you will not only be projected into the future but will also get a better understanding on how artificial intelligence already impacts our daily practice 1 . The second introductory article in this issue addresses a common problem faced by pediatricians, as registration of medications often comes with restrictions that specifically affect children.…”

“…by Ferrante and colleagues, you will not only be projected into the future but will also get a better understanding on how artificial intelligence already impacts our daily practice. 1 The second introductory article in this issue addresses a common problem faced by pediatricians, as registration of medications often comes with restrictions that specifically affect children. The use of pimecrolimus in the treatment of atopic dermatitis in infants is an excellent example of this, which is extensively commented by Luger and colleagues in this rostrum article.…”