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For: Doshi R, Falzon D, Thomas BV, Temesgen Z, Sadasivan L, Migliori GB, Raviglione M. Tuberculosis control, and the where and why of artificial intelligence. ERJ Open Res 2017;3:00056-2017. [PMID: 28656130 DOI: 10.1183/23120541.00056-2017] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Jonathan J, Barakabitze AA. ML technologies for diagnosing and treatment of tuberculosis: a survey. Health Technol 2023. [DOI: 10.1007/s12553-023-00727-5] [Reference Citation Analysis]
2 Attai K, Amannejad Y, Vahdat Pour M, Obot O, Uzoka FM. A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases. Trop Med Infect Dis 2022;7. [PMID: 36548653 DOI: 10.3390/tropicalmed7120398] [Reference Citation Analysis]
3 Otaigbe I. Scaling up artificial intelligence to curb infectious diseases in Africa. Front Digit Health 2022;4:1030427. [PMID: 36339519 DOI: 10.3389/fdgth.2022.1030427] [Reference Citation Analysis]
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6 Zhao Y, Souri A. Effect Evaluation of Artificial Intelligence-Based Electronic Health PDCA Nursing Model in the Treatment of Mycoplasma Pneumonia in Children. Journal of Healthcare Engineering 2022;2022:1-10. [DOI: 10.1155/2022/1956944] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Hung CM, Shi HY, Lee PH, Chang CS, Rau KM, Lee HM, Tseng CH, Pei SN, Tsai KJ, Chiu CC. Potential and role of artificial intelligence in current medical healthcare. Artif Intell Cancer 2022; 3(1): 1-10 [DOI: 10.35713/aic.v3.i1.1] [Reference Citation Analysis]
8 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]
9 Aboalshamat K, Alhuzali R, Alalyani A, Alsharif S, Qadhi H, Almatrafi R, Ammash D, Alotaibi S. Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030. Int J Pharm Res Allied Sci 2022;11:52-59. [DOI: 10.51847/nu8y6y6q1m] [Reference Citation Analysis]
10 Palomares I, Martínez-Cámara E, Montes R, García-Moral P, Chiachio M, Chiachio J, Alonso S, Melero FJ, Molina D, Fernández B, Moral C, Marchena R, de Vargas JP, Herrera F. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Appl Intell (Dordr) 2021;:1-31. [PMID: 34764606 DOI: 10.1007/s10489-021-02264-y] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
11 Badawi A, Liu CJ, Rehim AA, Gupta A. Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection. J Public Health Res 2021;10. [PMID: 33849253 DOI: 10.4081/jphr.2021.1985] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
12 Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study. BMC Med Educ 2021;21:112. [PMID: 33602196 DOI: 10.1186/s12909-021-02546-6] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
13 Van Der Walt M, Keddy KH. The Tuberculosis-Depression Syndemic and Evolution of Pharmaceutical Therapeutics: From Ancient Times to the Future. Front Psychiatry 2021;12:617751. [PMID: 34140898 DOI: 10.3389/fpsyt.2021.617751] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Bate A, Hobbiger SF. Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Saf 2021;44:125-32. [PMID: 33026641 DOI: 10.1007/s40264-020-01001-7] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
15 Chopra KK, Arora VK. Artificial intelligence and TB management - The way forward. Indian J Tuberc. 2020;67:1-2. [PMID: 32192602 DOI: 10.1016/j.ijtb.2020.02.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
16 Lan Z, Ahmad N, Baghaei P, Barkane L, Benedetti A, Brode SK, Brust JCM, Campbell JR, Chang VWL, Falzon D, Guglielmetti L, Isaakidis P, Kempker RR, Kipiani M, Kuksa L, Lange C, Laniado-Laborín R, Nahid P, Rodrigues D, Singla R, Udwadia ZF, Menzies D; Collaborative Group for the Meta-Analysis of Individual Patient Data in MDR-TB treatment 2017. Drug-associated adverse events in the treatment of multidrug-resistant tuberculosis: an individual patient data meta-analysis. Lancet Respir Med 2020;8:383-94. [PMID: 32192585 DOI: 10.1016/S2213-2600(20)30047-3] [Cited by in Crossref: 81] [Cited by in F6Publishing: 92] [Article Influence: 27.0] [Reference Citation Analysis]
17 Lee Y, Raviglione MC, Flahault A. Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review. J Med Internet Res 2020;22:e15727. [PMID: 32053111 DOI: 10.2196/15727] [Cited by in Crossref: 24] [Cited by in F6Publishing: 24] [Article Influence: 8.0] [Reference Citation Analysis]
18 Lee Y, Raviglione MC, Flahault A. Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review (Preprint).. [DOI: 10.2196/preprints.15727] [Reference Citation Analysis]
19 Chanchaichujit J, Tan A, Meng F, Eaimkhong S. Application of Artificial Intelligence in Healthcare. Healthcare 4.0 2019. [DOI: 10.1007/978-981-13-8114-0_4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
20 Katzman JH, Sampson M, Casañas B. Tuberculosis: Advances in Diagnostics and Treatment. Global Virology III: Virology in the 21st Century 2019. [DOI: 10.1007/978-3-030-29022-1_19] [Reference Citation Analysis]