All Outputs (3)

The radiography students' perspective of the impact of COVID-19 on education and training internationally: a across sectional survey of the UK Devolved Nations (UKDN) and the United Arab Emirates (UAE). (2022)
Journal Article
ELSHAMI, W., ABUZAID, M.M., MCCONNELL, J., STEWART, S., FLOYD, M., HUGHES, D., MCCLINTICK, C., ECKLOFF, K., LEISHMAN, L. and MCFADDEN, S. 2022. The radiography students' perspective of the impact of COVID-19 on education and training internationally: a across sectional survey of the UK Devolved Nations (UKDN) and the United Arab Emirates (UAE). Radiography [online], 28(Supplement 1), pages S50-S58). Available from: https://doi.org/10.1016/j.radi.2022.07.009

The overnight change in hospital practice and service delivery during the COVID-19 pandemic raises the question whether undergraduate radiography students received an adequate clinical experience. Many students had their clinical placements cancelled... Read More about The radiography students' perspective of the impact of COVID-19 on education and training internationally: a across sectional survey of the UK Devolved Nations (UKDN) and the United Arab Emirates (UAE)..

Late non-physiological impacts of Covid-19 on radiography education. (2021)
Journal Article
MCCONNELL, J., MCFADDEN, S., FLOYD, M., ELSHAMI, W., ABUZAID, M.M., LEISHMAN, L. and ECKLOFF, K. 2021. Late non-physiological impacts of COVID-19 on radiography education. Radiography [online], 27(3), pages 987-988. Available from: https://doi.org/10.1016/j.radi.2021.04.006

In this letter to the editor the authors say that there are long term questions which have not yet been published/investigated to address demands on staff and student radiographers during pandemic recovery and suggest that a special issue of 'Radiogr... Read More about Late non-physiological impacts of Covid-19 on radiography education..

Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection.
Presentation / Conference Contribution
MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the... Read More about Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection..