Literature DB >> 33220121

Artificial intelligence in the diagnosis of pediatric allergic diseases.

Giuliana Ferrante1, Amelia Licari2, Salvatore Fasola3, Gian Luigi Marseglia2, Stefania La Grutta3.   

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.
© 2020 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Entities:  

Keywords:  allergy; artificial intelligence; children; diagnosis; eczema; food allergy; respiratory allergy

Year:  2020        PMID: 33220121     DOI: 10.1111/pai.13419

Source DB:  PubMed          Journal:  Pediatr Allergy Immunol        ISSN: 0905-6157            Impact factor:   6.377


  3 in total

1.  Advancing artificial intelligence-assisted pre-screening for fragile X syndrome.

Authors:  Arezoo Movaghar; David Page; Murray Brilliant; Marsha Mailick
Journal:  BMC Med Inform Decis Mak       Date:  2022-06-10       Impact factor: 3.298

2.  APAAACI 2021 International Conference: a new era of allergy and clinical immunology in digital.

Authors:  Ruby Pawankar; Bernard Yu-Hor Thong; Jiu-Yao Wang
Journal:  Asia Pac Allergy       Date:  2022-01-18

3.  Endotyping allergic rhinitis in children: A machine learning approach.

Authors:  Velia Malizia; Giovanna Cilluffo; Salvatore Fasola; Giuliana Ferrante; Massimo Landi; Laura Montalbano; Amelia Licari; Stefania La Grutta
Journal:  Pediatr Allergy Immunol       Date:  2022-01       Impact factor: 5.464

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.