Author(s)

Emmanuel Worlali Fiagbedzi^1,2*, Philip Nii Gorleku¹, Savanna Nyarko¹, Vivian Della Atuwo-Ampoh³, Yawo Atsu Constantino Fiagan⁴, Adomako Asare⁵

¹Uuniversity of Cape Coast, Department of Medical Imaging, Cape Coast, Ghana.
²Department of Medical Physics, University of Ghana, Accra, Ghana.
³University of Health and Allied Science, Ho, Ghana.
⁴Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium.
⁵Department of Radiology, Komfo Anokye Teaching Hospital, Kumasi, Ghana.

Background: The medical imaging world is currently changing with the introduction of advanced modalities to help with diagnosis. There is then the need for the application of Artificial Intelligence (AI) in areas such as radiation protection to improve the safety as far as radiations are concerned. This review article discusses the principles, some of the challenges of radiation protection and the possible role of Artificial Intelligence (AI) regarding radiation protection in computed tomography and fluoroscopy exams. Methods: A literature search was done using Google Scholar, Science Direct and Pubmed to search for relevant articles concerning the review topic. Results: Some of the challenges identified were outdated and old X-ray machines, lack of QA programs on the machines amongst others. It was discovered that AI could be applied in areas like scan planning and positioning, patient positioning amongst others in CT imaging to reduce radiation doses. With fluoroscopy, an AI enabled system helped in reducing radiation doses by selecting the region of interest of pathology and exposing that region. Conclusion: The application of AI will improve safety and standards of practice in medical imaging.

Artificial Intelligence (AI), Radiations Protection, Computed Tomography, Fluoroscopy

Fiagbedzi, E. , Gorleku, P. , Nyarko, S. , Atuwo-Ampoh, V. , Fiagan, Y. and Asare, A. (2022) The Role of Artificial Intelligence (AI) in Radiation Protection of Computed Tomography and Fluoroscopy: A Review. Open Journal of Medical Imaging, 12, 25-36. doi: 10.4236/ojmi.2022.121004.