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For: Botwe BO, Antwi WK, Arkoh S, Akudjedu TN. Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study. J Med Radiat Sci 2021;68:260-8. [PMID: 33586361 DOI: 10.1002/jmrs.460] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [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 Aldhafeeri FM. Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia. Insights Imaging 2022;13:178. [DOI: 10.1186/s13244-022-01319-z] [Reference Citation Analysis]
4 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]
5 Rainey C, O'regan T, Matthew J, Skelton E, Woznitza N, Chu K, Goodman S, Mcconnell J, Hughes C, Bond R, Malamateniou C, Mcfadden S. An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey. Journal of Medical Imaging and Radiation Sciences 2022;53:347-361. [DOI: 10.1016/j.jmir.2022.05.010] [Reference Citation Analysis]
6 Rainey C, O'Regan T, Matthew J, Skelton E, Woznitza N, Chu KY, Goodman S, McConnell J, Hughes C, Bond R, Malamateniou C, McFadden S. UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments. Radiography (Lond) 2022;28:881-8. [PMID: 35780627 DOI: 10.1016/j.radi.2022.06.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 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]
8 Alyami AS, Majrashi NA, Shubayr NA. Radiologists’ and radiographers’ perspectives on artificial intelligence in medical imaging in Saudi Arabia (Preprint).. [DOI: 10.2196/preprints.35765] [Reference Citation Analysis]
9 Rainey C, O'Regan T, Matthew J, Skelton E, Woznitza N, Chu KY, Goodman S, McConnell J, Hughes C, Bond R, McFadden S, Malamateniou C. Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers. Front Digit Health 2021;3:739327. [PMID: 34859245 DOI: 10.3389/fdgth.2021.739327] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
10 Botwe BO, Antwi WK, Adusei JA, Mayeden RN, Akudjedu TN, Sule SD. COVID-19 vaccine hesitancy concerns: Findings from a Ghana clinical radiography workforce survey. Radiography (Lond) 2021:S1078-8174(21)00149-8. [PMID: 34654631 DOI: 10.1016/j.radi.2021.09.015] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
11 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]
12 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]
13 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]
14 Botwe BO, Akudjedu TN, Antwi WK, Rockson P, Mkoloma SS, Balogun EO, Elshami W, Bwambale J, Barare C, Mdletshe S, Yao B, Arkoh S. The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers. Radiography (Lond) 2021;27:861-6. [PMID: 33622574 DOI: 10.1016/j.radi.2021.01.008] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 4.5] [Reference Citation Analysis]