Literature DB >> 35300959

A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.

Paneez Khoury1, Renganathan Srinivasan2, Sujani Kakumanu3, Sebastian Ochoa4, Anjeni Keswani5, Rachel Sparks6, Nicholas L Rider7.   

Abstract

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists. Published by Elsevier Inc.

Entities:  

Keywords:  Artificial intelligence; Asthma; Atopic dermatitis; Augmented intelligence; Clinical decision support; Electronic health records; Equity; Machine learning; Medical education; Natural language processing; Primary immunodeficiency

Mesh:

Year:  2022        PMID: 35300959      PMCID: PMC9205719          DOI: 10.1016/j.jaip.2022.01.047

Source DB:  PubMed          Journal:  J Allergy Clin Immunol Pract


  80 in total

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Review 9.  Resolving Clinical Phenotypes into Endotypes in Allergy: Molecular and Omics Approaches.

Authors:  Tesfaye B Mersha; Yashira Afanador; Elisabet Johansson; Steven P Proper; Jonathan A Bernstein; Marc E Rothenberg; Gurjit K Khurana Hershey
Journal:  Clin Rev Allergy Immunol       Date:  2021-04       Impact factor: 8.667

Review 10.  The need for a system view to regulate artificial intelligence/machine learning-based software as medical device.

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