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Cited by in F6Publishing
For: Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 2020;75:695-701. [PMID: 32409611 DOI: 10.1136/thoraxjnl-2020-214556] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
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2 Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respir Res 2020;21:253. [PMID: 32993620 DOI: 10.1186/s12931-020-01523-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
3 Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopoulos P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers (Basel). 2020;12. [PMID: 32560475 DOI: 10.3390/cancers12061604] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
4 Reddy R. Imaging diagnosis of bronchogenic carcinoma (the forgotten disease) during times of COVID-19 pandemic: Current and future perspectives. World J Clin Oncol 2021; 12(6): 437-457 [PMID: 34189068 DOI: 10.5306/wjco.v12.i6.437] [Reference Citation Analysis]
5 Filipow N, Main E, Sebire NJ, Booth J, Taylor AM, Davies G, Stanojevic S. Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. BMJ Open Respir Res 2022;9:e001165. [PMID: 35297371 DOI: 10.1136/bmjresp-2021-001165] [Reference Citation Analysis]
6 Cilloniz C, Torres A. What's Next in Pneumonia? Arch Bronconeumol 2022;58:208-10. [PMID: 35312596 DOI: 10.1016/j.arbres.2021.08.006] [Reference Citation Analysis]
7 Wu F, Zhou Y, Peng J, Deng Z, Wen X, Wang Z, Zheng Y, Tian H, Yang H, Huang P, Zhao N, Sun R, Chen R, Ran P. Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study. J Thorac Dis 2021;13:6924-35. [PMID: 35070376 DOI: 10.21037/jtd-21-1379] [Reference Citation Analysis]
8 Zhang O, Minku LL, Gonem S. Detecting asthma exacerbations using daily home monitoring and machine learning. J Asthma 2020;:1-10. [PMID: 32718193 DOI: 10.1080/02770903.2020.1802746] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
9 Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, Kostikas K, Price D, Reddel HK, Tsiligianni I, Vogelmeier CF, Pfister P, Mastoridis P. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. J Allergy Clin Immunol Pract 2021;9:2255-61. [PMID: 33618053 DOI: 10.1016/j.jaip.2021.02.014] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
10 Kim Y, Park JY, Hwang EJ, Lee SM, Park CM. Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology. J Thorac Dis 2021;13:6943-62. [PMID: 35070379 DOI: 10.21037/jtd-21-1342] [Reference Citation Analysis]
11 Darbari A, Kumar K, Darbari S, Patil PL. Requirement of artificial intelligence technology awareness for thoracic surgeons. Cardiothorac Surg 2021;29. [DOI: 10.1186/s43057-021-00053-4] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022;29 Suppl 2:S226-35. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Khemasuwan D, Sorensen JS, Colt HG. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir Rev 2020;29:200181. [PMID: 33004526 DOI: 10.1183/16000617.0181-2020] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]
14 Liao KM, Liu CF, Chen CJ, Shen YT. Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease. Diagnostics (Basel) 2021;11:2396. [PMID: 34943632 DOI: 10.3390/diagnostics11122396] [Reference Citation Analysis]
15 Bock S, Rades T, Rantanen J, Scherließ R. Additive manufacturing in respiratory sciences - Current applications and future prospects. Adv Drug Deliv Rev 2022;186:114341. [PMID: 35569558 DOI: 10.1016/j.addr.2022.114341] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Zhou T, Yuan Z, Weng J, Pei D, Du X, He C, Lai P. Challenges and advances in clinical applications of mesenchymal stromal cells. J Hematol Oncol. 2021;14:24. [PMID: 33579329 DOI: 10.1186/s13045-021-01037-x] [Cited by in Crossref: 7] [Cited by in F6Publishing: 13] [Article Influence: 7.0] [Reference Citation Analysis]