TY - JOUR
T1 - Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology
AU - Edzie, Emmanuel Kobina Mesi
AU - Dzefi-Tettey, Klenam
AU - Asemah, Abdul Raman
AU - Brakohiapa, Edmund Kwakye
AU - Asiamah, Samuel
AU - Quarshie, Frank
AU - Amankwa, Adu Tutu
AU - Raj, Amrit
AU - Nimo, Obed
AU - Boadi, Evans
AU - Kpobi, Joshua Mensah
AU - Edzie, Richard Ato
AU - Osei, Bernard
AU - Turkson, Veronica
AU - Kusodzi, Henry
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Ghana
KW - Machine learning
KW - Perception
KW - Radiology
UR - http://www.scopus.com/inward/record.url?scp=85152892711&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2023.e15558
DO - 10.1016/j.heliyon.2023.e15558
M3 - Article
AN - SCOPUS:85152892711
SN - 2405-8440
VL - 9
JO - Heliyon
JF - Heliyon
IS - 5
M1 - e15558
ER -