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For: Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, Ninane V, Pison C, Schlesser M, Vercauter P, Vogelmeier CF, Wouters E, Wynants J, Janssens W; Pulmonary Function Study Investigators., Pulmonary Function Study Investigators:. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 2019;53:1801660. [PMID: 30765505 DOI: 10.1183/13993003.01660-2018] [Cited by in Crossref: 54] [Cited by in F6Publishing: 59] [Article Influence: 13.5] [Reference Citation Analysis]
Number Citing Articles
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2 Mehrpour O, Nakhaee S, Saeedi F, Valizade B, Lotfi E, Nawaz MH. Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS). Environ Sci Pollut Res Int 2023. [PMID: 36973614 DOI: 10.1007/s11356-023-26605-1] [Reference Citation Analysis]
3 Briganti G. [Artificial intelligence: An introduction for clinicians]. Rev Mal Respir 2023:S0761-8425(23)00090-6. [PMID: 36894376 DOI: 10.1016/j.rmr.2023.02.005] [Reference Citation Analysis]
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6 Winck JC, Ambrosino N. Tele-Medicine: The Search of the Holy Grail. Arch Bronconeumol 2023:S0300-2896(23)00026-1. [PMID: 36803936 DOI: 10.1016/j.arbres.2023.01.014] [Reference Citation Analysis]
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8 Li HY, Gao TY, Fang W, Xian-Yu CY, Deng NJ, Zhang C, Niu YM. Global, regional and national burden of chronic obstructive pulmonary disease over a 30-year period: Estimates from the 1990 to 2019 Global Burden of Disease Study. Respirology 2023;28:29-36. [PMID: 36054068 DOI: 10.1111/resp.14349] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Wijsenbeek MS, Moor CC, Johannson KA, Jackson PD, Khor YH, Kondoh Y, Rajan SK, Tabaj GC, Varela BE, van der Wal P, van Zyl-Smit RN, Kreuter M, Maher TM. Home monitoring in interstitial lung diseases. Lancet Respir Med 2023;11:97-110. [PMID: 36206780 DOI: 10.1016/S2213-2600(22)00228-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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11 de Lima AD, Lopes AJ, do Amaral JLM, de Melo PL. Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. BMC Med Inform Decis Mak 2022;22:274. [PMID: 36266674 DOI: 10.1186/s12911-022-02021-2] [Reference Citation Analysis]
12 Yu G, Tabatabaei M, Mezei J, Zhong Q, Chen S, Li Z, Li J, Shu L, Shu Q. Improving chronic disease management for children with knowledge graphs and artificial intelligence. Expert Systems with Applications 2022;201:117026. [DOI: 10.1016/j.eswa.2022.117026] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Almazloum AA, Al-hinnawi A, De Fazio R, Visconti P. Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test’s Diagnosis Using ATS and ERS Respiratory Standard Parameters. Computers 2022;11:130. [DOI: 10.3390/computers11090130] [Reference Citation Analysis]
14 Zhou J, Wang P, Guo L, Cao J, Zhou M, Dai R. Automated interpretation of the pulmonary function test by a portable spirometer in Chinese adults. Clin Respir J 2022. [PMID: 35869604 DOI: 10.1111/crj.13525] [Reference Citation Analysis]
15 Thurzo A, Urbanová W, Novák B, Czako L, Siebert T, Stano P, Mareková S, Fountoulaki G, Kosnáčová H, Varga I. Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022;10:1269. [PMID: 35885796 DOI: 10.3390/healthcare10071269] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
16 Beckman L, Rosenberg JH. The Democratic Inclusion of Artificial Intelligence? Exploring the Patiency, Agency and Relational Conditions for Demos Membership. Philos Technol 2022;35:24. [DOI: 10.1007/s13347-022-00525-3] [Reference Citation Analysis]
17 Avdeev SN, Emelyanov AV, Aisanov ZR, Sinopalnikov AI, Fomina DS, Nenasheva NM, Leshchenko IV, Zaikova-khelimskaia IV, Vizel AA, Demko IV, Shaporova NL, Shulzhenko LV, Shabanov EA. Problems and opportunities to improve diagnosis of asthma and chronic obstructive pulmonary disease in Russia: resolution of advisory board. Terapevticheskii arkhiv 2022;94:524-9. [DOI: 10.26442/00403660.2022.04.201487] [Reference Citation Analysis]
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19 Lo Y, Varghese S, Blackley S, Seger DL, Blumenthal KG, Goss FR, Zhou L. Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing. Front Allergy 2022;3:904923. [DOI: 10.3389/falgy.2022.904923] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Wang Y, Li Y, Chen W, Zhang C, Liang L, Huang R, Liang J, Tu D, Gao Y, Zheng J, Zhong N. Deep learning for spirometry quality assurance with spirometric indices and curves. Respir Res 2022;23:98. [PMID: 35448995 DOI: 10.1186/s12931-022-02014-9] [Reference Citation Analysis]
21 Ramakrishnan S, Beaufils F, De Brandt J, Viney K, Bradley C, Cottin V, Hassan M, Cruz J. European Respiratory Society International Congress 2021: highlights from best-abstract awardees. Breathe (Sheff) 2022;18:210176. [PMID: 36338250 DOI: 10.1183/20734735.0176-2021] [Reference Citation Analysis]
22 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
23 Persaud YK. Using Telemedicine to Care for the Asthma Patient. Curr Allergy Asthma Rep 2022. [PMID: 35107807 DOI: 10.1007/s11882-022-01030-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Wang Y, Li Q, Chen W, Jian W, Liang J, Gao Y, Zhong N, Zheng J. Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns. Front Physiol 2022;13:824000. [DOI: 10.3389/fphys.2022.824000] [Reference Citation Analysis]
25 Das N, Topalovic M, Janssens W. AIM in Respiratory Disorders. Artificial Intelligence in Medicine 2022. [DOI: 10.1007/978-3-030-64573-1_178] [Reference Citation Analysis]
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27 Althobiani MA, Evans RA, Alqahtani JS, Aldhahir AM, Russell AM, Hurst JR, Porter JC. Home monitoring of physiology and symptoms to detect interstitial lung disease exacerbations and progression: a systematic review. ERJ Open Res 2021;7:00441-2021. [PMID: 34938799 DOI: 10.1183/23120541.00441-2021] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Wang Y, Chen W, Li Y, Zhang C, Liang L, Huang R, Liang J, Gao Y, Zheng J. Clinical analysis of the "small plateau" sign on the flow-volume curve followed by deep learning automated recognition. BMC Pulm Med 2021;21:359. [PMID: 34753450 DOI: 10.1186/s12890-021-01733-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
29 Seastedt KP, Moukheiber D, Mahindre SA, Thammineni C, Rosen DT, Watkins AA, Hashimoto DA, Hoang CD, Kpodonu J, Celi LA. A scoping review of artificial intelligence applications in thoracic surgery. Eur J Cardiothorac Surg 2021:ezab422. [PMID: 34601587 DOI: 10.1093/ejcts/ezab422] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
30 Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, Henriquez C, MacIntyre NR, Huang YT. Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol 2021;12:678540. [PMID: 34248665 DOI: 10.3389/fphys.2021.678540] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
31 Exarchos KP, Kostikas K. Artificial intelligence in COPD: Possible applications and future prospects. Respirology 2021;26:641-2. [PMID: 33851496 DOI: 10.1111/resp.14061] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Glangetas A, Hartley MA, Cantais A, Courvoisier DS, Rivollet D, Shama DM, Perez A, Spechbach H, Trombert V, Bourquin S, Jaggi M, Barazzone-Argiroffo C, Gervaix A, Siebert JN. Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study. BMC Pulm Med 2021;21:103. [PMID: 33761909 DOI: 10.1186/s12890-021-01467-w] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
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35 Smith E, Thomas M, Calik-Kutukcu E, Torres-Sánchez I, Granados-Santiago M, Quijano-Campos JC, Sylvester K, Burtin C, Sajnic A, De Brandt J, Cruz J. ERS International Congress 2020 Virtual: highlights from the Allied Respiratory Professionals Assembly. ERJ Open Res 2021;7:00808-2020. [PMID: 33585651 DOI: 10.1183/23120541.00808-2020] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
36 Feng Y, Wang Y, Zeng C, Mao H. Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease. Int J Med Sci 2021;18:2871-89. [PMID: 34220314 DOI: 10.7150/ijms.58191] [Cited by in Crossref: 17] [Cited by in F6Publishing: 16] [Article Influence: 8.5] [Reference Citation Analysis]
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38 Wilhelm D, Müller-stich B, Ostler D, Schmitz-rixen T, Feussner H. Positionspapier „Digitalisierung in der Chirurgie“ – Konsequenzen? Zentralbl Chir 2020;145:495-498. [DOI: 10.1055/a-1030-3888] [Reference Citation Analysis]
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47 She Y, Jin Z, Wu J, Deng J, Zhang L, Su H, Jiang G, Liu H, Xie D, Cao N, Ren Y, Chen C. Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival. JAMA Netw Open 2020;3:e205842. [PMID: 32492161 DOI: 10.1001/jamanetworkopen.2020.5842] [Cited by in Crossref: 53] [Cited by in F6Publishing: 49] [Article Influence: 17.7] [Reference Citation Analysis]
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