The Applications of Artificial Intelligence in Radiography and the Necessary Changes in the Education of Radiographers

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Published: Jul 1, 2022
DOI: https://doi.org/10.12681/akttech.40673
Keywords:
Artificial Intellignece Technology Healthcare Education
Nikolaos Stogiannos
Department of Midwifery & Radiography, City St. George's, University of London, UK
Eleni Georgiadou
"Metaxa" Cancer Hospital of Piraeus, Greece
Charalabos Bougias
University General Hospital of Ioannina, Greece
Christina Malamateniou
Department of Midwifery & Radiography, City St George's, University of London, UK
Abstract

Artificial Intelligence (AI), which was firstly introduced in the 1950s, has a multidisciplinary character, bringing together computer science, applied mathematics, physics etc., while it also feeds and promotes many technology related fields. Specifically, the use of AI in healthcare has already started to produce great advantages and procedures which lead to better outcomes.


In medical imaging departments, there are now some AI-based systems which can optimise image quality, facilitate automated treatment planning and automated image reconstruction, achieve automated examination planning, reduced scan time, structured reporting etc. Radiographers must use now these AI-based systems in their daily practice, either in radiography or radiotherapy.


This technology is now present in every aspect of our daily lives, including healthcare. The only way to achieve a smooth inclusion is by enhancing and improving radiography-related academic curricula. It is therefore vital to decide how we are going to face this new era.

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References
Noguerol TM, Paulano-Godino F, Martin-Valdivia MT, Menias CO, Luna A. Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. J Am Coll Radiol 2019;16(9):1239-1247. https://doi.org/10.1016/j.jacr.2019.05.047
Pinto dos Santos D, Brodehl S, Baebler B, Arnhold G, Dratsch T, Chon S-H, et al. Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs. Insights Imaging 2019;10(1):93. https://doi.org/10.1186/s13244-019-0777-8
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500-510. https://doi.org/10.1038/s41568-018-0016-5
Lewis SJ, Gandomkar Z, Brennan PC. Artificial Intelligence in medical imaging practice: looking to the future. J Med Radiat Sci 2019;66(4):292-295. https://doi.org/10.1002/jmrs.369
Hardy M, Harvey H. Artificial intelligence in diagnostic imaging: impact on the radiography profession. Br J Radiol 2020;93(1108):20190840. https://doi.org/10.1259/bjr.20190840
Malamateniou C, Knapp KM, Pergola M, Woznitza N, Hardy M. Artificial intelligence in radiography: Where are we now and what does the future hold? Radiography 2021;27(suppl 1):S58-S62. https://doi.org/10.1016/j.radi.2021.07.0157
Higaki T, Nakamura Y, Tatsugami F, Nakaura T, Awai K. Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol 2019;37(1):73-80. https://doi.org/10.1007/s11604-018-0796-2
Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: walking towards fully-automated machine analysis & interpretation. Expert Rev Med Devices 2017;149(3):197-212. https://doi.org/10.1080/17434440.2017.1300057
The Society of Radiographers (2021) Why radiographers should be driving forward AI. https://www.sor.org/news/cpd/why-radiographers-should-be-driving-forward-ai
Rainey C, O’Regan T, Matthew J, Skelton E, Woznitza N, Chu KY, et al. 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. https://doi.org/10.3389/fdgth.2021.739327
Malamateniou C, McFadden S, McQuinlan Y, England A, Woznitza N, Goldsworthy S, et al. Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI Working Group. Radiography 2021;27(4):1192-1202. https://doi.org/10.1016/j.radi.2021.07.028
Bougias H, Georgiadou E, Malamateniou C, Stogiannos N. Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods. Acta Radiol 2021;62(12):1601-1609. https://doi.org/10.1177/0284185120973630