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For: Antwi WK, Akudjedu TN, Botwe BO. Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers' perspectives. Insights Imaging 2021;12:80. [PMID: 34149958 DOI: 10.1186/s13244-021-01028-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 9] [Article Influence: 3.5] [Reference Citation Analysis]
Number Citing Articles
1 Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. J Med Imaging Radiat Sci 2023;54:104-16. [PMID: 36535859 DOI: 10.1016/j.jmir.2022.11.016] [Reference Citation Analysis]
2 Ng CT, Roslan SNA, Chng YH, Choong DAW, Chong AJL, Tay YX, Lança L, Chua EC. Singapore radiographers' perceptions and expectations of artificial intelligence - A qualitative study. J Med Imaging Radiat Sci 2022;53:554-63. [PMID: 36115823 DOI: 10.1016/j.jmir.2022.08.005] [Reference Citation Analysis]
3 Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med 2022;5:162. [PMID: 36307479 DOI: 10.1038/s41746-022-00700-y] [Reference Citation Analysis]
4 Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. Physical Sciences Reviews 2022;0. [DOI: 10.1515/psr-2022-0121] [Reference Citation Analysis]
5 Currie G, Nelson T, Hewis J, Chandler A, Spuur K, Nabasenja C, Thomas C, Wheat J. Australian perspectives on artificial intelligence in medical imaging. J Med Radiat Sci 2022;69:282-92. [PMID: 35429129 DOI: 10.1002/jmrs.581] [Reference Citation Analysis]
6 Kumsa MJ, Lemu BN, Nguse TM, Omiyi DO, Akudjedu TN. Clinical placement challenges associated with radiography education in a low-resource setting: A qualitative exploration of the Ethiopian landscape. Radiography (Lond) 2022;28:634-40. [PMID: 35569316 DOI: 10.1016/j.radi.2022.04.014] [Reference Citation Analysis]
7 Ping Z, Liu Y, Guirao JLG. Classification and Visual Design Analysis of Network Expression Based on Big Data Multimodal Intelligence Technology. Discrete Dynamics in Nature and Society 2022;2022:1-7. [DOI: 10.1155/2022/7542606] [Reference Citation Analysis]
8 Elkefi S, Layeb SB. Artificial Intelligence and Operations Research in a Middle Ground to Support Decision-Making in Healthcare Systems in Africa. Africa Case Studies in Operations Research 2022. [DOI: 10.1007/978-3-031-17008-9_3] [Reference Citation Analysis]
9 Beegle C, Hasani N, Maass-Moreno R, Saboury B, Siegel E. Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective. PET Clin 2022;17:31-9. [PMID: 34809867 DOI: 10.1016/j.cpet.2021.09.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 DeStigter K, Pool KL, Leslie A, Hussain S, Tan BS, Donoso-Bach L, Andronikou S. Optimizing integrated imaging service delivery by tier in low-resource health systems. Insights Imaging 2021;12:129. [PMID: 34529166 DOI: 10.1186/s13244-021-01073-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
11 Wuni AR, Botwe BO, Akudjedu TN. Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting. Radiography (Lond) 2021:S1078-8174(21)00101-2. [PMID: 34400083 DOI: 10.1016/j.radi.2021.07.021] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]