• Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022 Jan 20;28(1):31–8. Review of major advances and trends of AI in medicine over the past two years with the discussion of promising avenues for future medical AI research.
Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial intelligence in cardiology: present and future. Mayo Clin Proc. 2020;95(5):1015–39.
•• Rider NL, Srinivasan R, Khoury P. Artificial intelligence and the hunt for immunological disorders. Curr Opin Allergy Clin Immunol. 2020;20(6). Review highlighting artificial intelligence applications in clinical immunology.
Ferrante G, Licari A, Fasola S, Marseglia GL, La Grutta S. Artificial intelligence in the diagnosis of pediatric allergic diseases. Pediatr Allergy Immunol. 2021;32(3):405–13.
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.
Matheny M. Artificial intelligence in health care: the hope, the hype, the promise, the peril. National Academy of Medicine2; 2019.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
Article CAS PubMed Google Scholar
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9.
Article CAS PubMed Google Scholar
Rudrapatna VA, Butte AJ. Opportunities and challenges in using real-world data for health care. J Clin Invest. 2020;130(2):565–74.
Article PubMed PubMed Central Google Scholar
Khoury P, Srinivasan R, Kakumanu S, Ochoa S, Keswani A, Sparks R, et al. 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. J Allergy Clin Immunol Pract. 2022;10(5):1178–88.
• Sagheb E, Wi C-I, Yoon J, Seol HY, Shrestha P, Ryu E, et al. Artificial intelligence assesses clinicians’ adherence to asthma guidelines using electronic health records. J Allergy Clin Immunol Pract. 2022 Apr;10(4):1047–1056.e1. Demonstration of the use of NLP to assess clinician adherence to asthma guidelines in a retrospective cross-sectional study evaluating 1039 clinical notes for 300 patients with an asthma diagnosis.
Davenport T. Using AI to improve electronic health records [Internet]. Harvard Business Review. 2018 [cited 2020 Dec 13]. Available from: https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Article CAS PubMed Google Scholar
Joumaa H, Sigogne R, Maravic M, Perray L, Bourdin A, Roche N. Artificial intelligence to differentiate asthma from COPD in medico-administrative databases. BMC Pulm Med. 2022;22(1):357.
Article PubMed PubMed Central Google Scholar
Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, et al. Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J Allergy Clin Immunol Pract. 2021;9(6):2255–61.
Article CAS PubMed Google Scholar
Hurst JH, Zhao C, Hostetler HP, Ghiasi Gorveh M, Lang JE, Goldstein BA. Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. BMC Med Inform Decis Mak. 2022;22(1):108.
Article PubMed PubMed Central Google Scholar
Jiao T, Schnitzer ME, Forget A, Blais L. Identifying asthma patients at high risk of exacerbation in a routine visit: a machine learning model. Respir Med. 2022;198: 106866.
Inselman JW, Jeffery MM, Maddux JT, Lam RW, Shah ND, Rank MA, et al. A prediction model for asthma exacerbations after stopping asthma biologics. Ann Allergy Asthma Immunol. 2022 Dec 9.
D’Amato M, Ambrosino P, Simioli F, Adamo S, Stanziola AA, D’Addio G, et al. A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting. Eur J Intern Med. 2022;104:66–72.
Lugogo NL, DePietro M, Reich M, Merchant R, Chrystyn H, Pleasants R, et al. A predictive machine learning tool for asthma exacerbations: results from a 12-week, open-label study using an electronic multi-dose dry powder inhaler with integrated sensors. J Asthma Allergy. 2022;11(15):1623–37.
Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA. Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open. 2022;12(10): e064166.
Article PubMed PubMed Central Google Scholar
Bae WD, Alkobaisi S, Horak M, Park C-S, Kim S, Davidson J. Predicting health risks of adult asthmatics susceptible to indoor air quality using improved logistic and quantile regression models. Life (Basel). 2022 Oct 18;12(10).
Ray A, Das J, Wenzel SE. Determining asthma endotypes and outcomes: complementing existing clinical practice with modern machine learning. Cell Rep Med. 2022;3(12): 100857.
Article PubMed PubMed Central Google Scholar
Bose S, Kenyon CC, Masino AJ. Personalized prediction of early childhood asthma persistence: a machine learning approach. PLoS ONE. 2021;16(3): e0247784.
Article CAS PubMed PubMed Central Google Scholar
Iqbal FM, Lam K, Joshi M, Khan S, Ashrafian H, Darzi A. Clinical outcomes of digital sensor alerting systems in remote monitoring: a systematic review and meta-analysis. npj Digital Med. 2021 Jan 8;4(1):7.
Eikholt AA, Wiertz MBR, Hew M, Chan AHY, van Boven JFM. Electronic monitoring devices to support inhalation technique in patients with asthma: a narrative review. Curr Treat Options Allergy. 2023 Feb 17.
Duverdier A, Custovic A, Tanaka RJ. Data-driven research on eczema: systematic characterization of the field and recommendations for the future. Clin Transl Allergy. 2022;12(6): e12170.
Article PubMed PubMed Central Google Scholar
De A, Sarda A, Gupta S, Das S. Use of artificial intelligence in dermatology. Indian J Dermatol. 2020;65(5):352–7.
Article PubMed PubMed Central Google Scholar
Eyerich K, Brown SJ, Perez White BE, Tanaka RJ, Bissonette R, Dhar S, et al. Human and computational models of atopic dermatitis: a review and perspectives by an expert panel of the International Eczema Council. J Allergy Clin Immunol. 2019;143(1):36–45.
Aggarwal SLP. Data augmentation in dermatology image recognition using machine learning. Skin Res Technol. 2019;25(6):815–20.
Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26(6):900–8.
Article CAS PubMed Google Scholar
Rasheed A, Umar AI, Shirazi SH, Khan Z, Nawaz S, Shahzad M. Automatic eczema classification in clinical images based on hybrid deep neural network. Comput Biol Med. 2022;147: 105807.
Guimarães P, Batista A, Zieger M, Kaatz M, Koenig K. Artificial intelligence in multiphoton tomography: atopic dermatitis diagnosis. Sci Rep. 2020;10(1):7968.
Article PubMed PubMed Central Google Scholar
Park S, Saw SN, Li X, Paknezhad M, Coppola D, Dinish US, et al. Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis. Biomed Opt Express. 2021;12(6):3671–83.
Article PubMed PubMed Central Google Scholar
Aguirre J, Schwarz M, Garzorz N, Omar M, Buehler A, Eyerich K, et al. Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy. Nat Biomed Eng. 2017;1(5):0068.
Schmitt J, Langan S, Deckert S, Svensson A, von Kobyletzki L, Thomas K, et al. Assessment of clinical signs of atopic dermatitis: a systematic review and recommendation. J Allergy Clin Immunol. 2013;132(6):1337–47.
Medela A, Mac Carthy T, Aguilar Robles SA, Chiesa-Estomba CM, Grimalt R. Automatic scoring of atopic dermatitis using deep learning: a pilot study. JID Innov. 2022;2(3): 100107.
Article PubMed PubMed Central Google Scholar
Hurault G, Domínguez-Hüttinger E, Langan SM, Williams HC, Tanaka RJ. Personalized prediction of daily eczema severity scores using a mechanistic machine learning model. Clin Exp Allergy. 2020;50(11):1258–66.
Hurault G, Stalder JF, Mery S, Delarue A, Saint Aroman M, Josse G, et al. EczemaPred: A computational framework for personalised prediction of eczema severity dynamics. Clin Transl Allergy. 2022;12(3): e12140.
Article PubMed PubMed Central Google Scholar
Holm JG, Hurault G, Agner T, Clausen ML, Kezic S, Tanaka RJ, et al. Immunoinflammatory biomarkers in serum are associated with disease severity in atopic dermatitis. Dermatology (Basel). 2021;237(4):513–20.
Maintz L, Welchowski T, Herrmann N, Brauer J, Kläschen AS, Fimmers R, et al. Machine learning-based deep phenotyping of atopic dermatitis: severity-associated factors in adolescent and adult patients. JAMA Dermatol. 2021;157(12):1414–24.
Clayton K, Vallejo A, Sirvent S, Davies J, Porter G, Reading IC, et al. Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotype. Br J Dermatol. 2021;184(5):913–22.
Article CAS PubMed Google Scholar
Acharjee A, Gribaleva E, Bano S, Gkoutos GV. Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs. Am J Transl Res. 2021;13(12):13697–709.
CAS PubMed PubMed Central Google Scholar
Jiang Z, Li J, Kong N, Kim J-H, Kim B-S, Lee M-J, et al. Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning. Sci Rep. 2022;12(1):290.
Article PubMed PubMed Central Google Scholar
Zeldin J, Chaudhary PP, Spathies J, Yadav M, D’Souza BN, Alishahedani ME, et al. Exposure to isocyanates predicts atopic dermatitis prevalence and disrupts therapeutic pathways in commensal bacteria. Sci Adv. 2023 Jan 6;9(1):eade8898.
Fadadu RP, Grimes B, Jewell NP, Vargo J, Young AT, Abuabara K, et al. Association of wildfire air pollution and health care use for atopic dermatitis and itch. JAMA Dermatol. 2021;157(6):658–66.
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