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

被引:27
作者
Khoury, Paneez [1 ]
Srinivasan, Renganathan [2 ]
Kakumanu, Sujani [3 ,4 ]
Ochoa, Sebastian [5 ,6 ]
Keswani, Anjeni [7 ]
Sparks, Rachel [5 ,8 ]
Rider, Nicholas L. [9 ,10 ]
机构
[1] NIAID, Lab Allerg Dis, 9000 Rockville Pike, Bethesda, MD 20892 USA
[2] Vancouver Clin, Vancouver, WA USA
[3] Univ Wisconsin, Sch Med & Publ Hlth, Div Allergy Pulm & Crit Care Med, Madison, WI USA
[4] William S Middleton Vet Mem Hosp, Madison, WI USA
[5] NIAID, 9000 Rockville Pike, Bethesda, MD 20892 USA
[6] NIAID, Lab Clin Immunol & Microbiol, 9000 Rockville Pike, Bethesda, MD 20892 USA
[7] George Washington Univ, Sch Med & Hlth Sci, Div Allergy Immunol, Washington, DC 20052 USA
[8] NIAID, Lab Immune Syst Biol, 9000 Rockville Pike, Bethesda, MD 20892 USA
[9] Texas Childrens Hosp, Baylor Coll Med, Sect Immunol Allergy & Retrovirol, Houston, TX 77030 USA
[10] Texas Childrens Hosp, Baylor Coll Med, William T Shearer Ctr Human Immunobiol, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
Artificial intelligence; Asthma; Primary immunodeficiency; Atopic dermatitis; Augmented intelligence; Clinical decision support; Electronic health records; Equity; Machine learning; Natural language processing; Medical education; ARTIFICIAL-INTELLIGENCE; ASTHMA; CHALLENGES; ALGORITHM; OMICS;
D O I
10.1016/j.jaip.2022.01.047
中图分类号
R392 [医学免疫学];
学科分类号
100102 ;
摘要
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. on behalf of the American Academy of Allergy, Asthma & Immunology
引用
收藏
页码:1178 / 1188
页数:11
相关论文
共 88 条
[1]   Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices [J].
Abramoff, Michael D. ;
Lavin, Philip T. ;
Birch, Michele ;
Shah, Nilay ;
Folk, James C. .
NPJ DIGITAL MEDICINE, 2018, 1
[2]   Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma: a Practall document [J].
Agache, Ioana ;
Miller, Rachel ;
Gern, James E. ;
Hellings, Peter W. ;
Jutel, Marek ;
Muraro, Antonella ;
Phipatanakul, Wanda ;
Quirce, Santiago ;
Peden, David .
ALLERGY, 2019, 74 (03) :449-463
[3]   Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review [J].
Albahri, A. S. ;
Hamid, Rula A. ;
Alwan, Jwan K. ;
Al-qays, Z. T. ;
Zaidan, A. A. ;
Zaidan, B. B. ;
Albahri, A. O. S. ;
AlAmoodi, A. H. ;
Khlaf, Jamal Mawlood ;
Almahdi, E. M. ;
Thabet, Eman ;
Hadi, Suha M. ;
Mohammed, K., I ;
Alsalem, M. A. ;
Al-Obaidi, Jameel R. ;
Madhloom, H. T. .
JOURNAL OF MEDICAL SYSTEMS, 2020, 44 (07)
[4]   PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods [J].
Almonacid Sanchez, C. ;
Melero Moreno, C. ;
Quirce Gancedo, S. ;
Sanchez-Herrero, M. G. ;
Alvarez Gutierrez, F. J. ;
Banas Conejero, D. ;
Cardona, V ;
Soriano, J. B. .
JOURNAL OF INVESTIGATIONAL ALLERGOLOGY AND CLINICAL IMMUNOLOGY, 2021, 31 (04) :308-315
[5]  
American Medical Association, 2019, 2019 COUN MED ED REP
[6]   Mechanisms of the Development of Allergy (MeDALL): Introducing novel concepts in allergy phenotypes [J].
Anto, Josep M. ;
Bousquet, Jean ;
Akdis, Mubeccel ;
Auffray, Charles ;
Keil, Thomas ;
Momas, Isabelle ;
Postma, Dirkje S. ;
Valenta, Rudolf ;
Wickman, Magnus ;
Cambon-Thomsen, Anne ;
Haahtela, Tari ;
Lambrecht, Bart N. ;
Carlsen, Karin C. Lodrup ;
Koppelman, Gerard H. ;
Sunyer, Jordi ;
Zuberbier, Torsten ;
Annesi-Maesano, Isabelle ;
Arno, Albert ;
Bindslev-Jensen, Carsten ;
De Carlo, Giuseppe ;
Forastiere, Francesco ;
Heinrich, Joachim ;
Kowalski, Marek L. ;
Maier, Dieter ;
Melen, Erik ;
Smit, Henriette A. ;
Standl, Marie ;
Wright, John ;
Asarnoj, Anna ;
Benet, Marta ;
Ballardini, Natalia ;
Garcia-Aymerich, Judith ;
Gehring, Ulrike ;
Guerra, Stefano ;
Hohmann, Cynthia ;
Kull, Inger ;
Lupinek, Christian ;
Pinart, Mariona ;
Skrindo, Ingebjorg ;
Westman, Marit ;
Smagghe, Delphine ;
Akdis, Cezmi ;
Andersson, Niklas ;
Bachert, Claus ;
Ballereau, Stephane ;
Ballester, Ferran ;
Basagana, Xavier ;
Bedbrook, Anna ;
Bergstrom, Anna ;
von Berg, Andrea .
JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, 2017, 139 (02) :388-399
[7]   Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data [J].
Bae, Wan D. ;
Kim, Sungroul ;
Park, Choon-Sik ;
Alkobaisi, Shayma ;
Lee, Jongwon ;
Seo, Wonseok ;
Park, Jong Sook ;
Park, Sujung ;
Lee, Sangwoon ;
Lee, Jong Wook .
PLOS ONE, 2021, 16 (01)
[8]   Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models [J].
Banda, Juan M. ;
Seneviratne, Martin ;
Hernandez-Boussard, Tina ;
Shah, Nigam H. .
ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 1, 2018, 1 :53-68
[9]   Unsupervised machine learning reveals key immune cell subsets in COVID-19 rhinovirus infection, and cancer therapy [J].
Barone, Sierra M. ;
Paul, Alberta G. A. ;
Muehling, Lyndsey M. ;
Lannigan, Joanne A. ;
Kwok, William W. ;
Turner, Ronald B. ;
Woodfolk, Judith A. ;
Irish, Jonathan M. .
ELIFE, 2021, 10
[10]   Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease [J].
Bastarache, Lisa ;
Hughey, Jacob J. ;
Goldstein, Jeffrey A. ;
Bastraache, Julie A. ;
Das, Satya ;
Zaki, Neil Charles ;
Zeng, Chenjie ;
Tang, Leigh Anne ;
Roden, Dan M. ;
Denny, Joshua C. .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2019, 26 (12) :1437-1447