Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology

Emmanuel Kobina Mesi Edzie, Klenam Dzefi-Tettey, Abdul Raman Asemah, Edmund Kwakye Brakohiapa, Samuel Asiamah, Frank Quarshie, Adu Tutu Amankwa, Amrit Raj, Obed Nimo, Evans Boadi, Joshua Mensah Kpobi, Richard Ato Edzie, Bernard Osei, Veronica Turkson, Henry Kusodzi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Background: The integration of Artificial Intelligence (AI)-based technologies in medicine is advancing rapidly especially in the field of radiology. This however, is at a slow pace in Africa, hence, this study to evaluate the perspectives of Ghanaian radiologists. Methods: Data for this cross-sectional prospective study was collected between September and November 2021 through an online survey and entered into SPSS for analysis. A Mann–Whitney U test assisted in checking for possible gender differences in the mean Likert scale responses on the radiologists’ perspectives about AI in radiology. Statistical significance was set at P ≤ 0.05. Results: The study comprised 77 radiologists, with more males (71.4%). 97.4% were aware of the concept of AI, with their initial exposure via conferences (42.9%). The majority of the respondents had average awareness (36.4%) and below average expertise (44.2%) in radiological AI usage. Most of the participants (54.5%) stated, they do not use AI in their practices. The respondents disagreed that AI will ultimately replace radiologists in the near future (average Likert score = 3.49, SD = 1.096) and that AI should be an integral part of the training of radiologists (average Likert score = 1.91, SD = 0.830). Conclusion: Although the radiologists had positive opinions about the capabilities of AI, they exhibited an average awareness of and below average expertise in the usage of AI applications in radiology. They agreed on the potential life changing impact of AI and were of the view that AI will not replace radiologists but serve as a complement. There was inadequate radiological AI infrastructure in Ghana.

Original languageEnglish
Article numbere15558
JournalHeliyon
Volume9
Issue number5
DOIs
Publication statusPublished - May 2023

Keywords

  • Artificial intelligence
  • Ghana
  • Machine learning
  • Perception
  • Radiology

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