Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review
Abstract
:1. Introduction
2. Research Methods and Study Selection
2.1. Research Methods
2.2. Study Selection
2.3. Data Extraction and Analysis
3. Results Analysis
3.1. Articles That Addressed Barriers
3.2. Articles That Addressed Enablers
3.3. Articles That Addressed Barriers and Enablers
4. Discussion
Limitations and Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | Geographic Location, Year of Publication | Design of Study | Journal | Barriers Identified | Enablers Identified |
---|---|---|---|---|---|
Gong et al. [18] | Canada, 2019 | Survey | Academic Radiology | Anxiety related to displacement” (not “replacement”) of radiologists by AI. | ------------------------ |
Pinto Dos Santos et al. [19] | Europe, 2019 | Survey | European Radiology | Medical students’ skepticism about AI providing a definite diagnosis in radiology. | ------------------------ |
Strohm et al. [20] | The Netherlands, 2020 | Qualitative | European Radiology | Lack of acceptance and trust of radiologists towards AI, unstructured implementation process. | ------------------------ |
Lim et al. [21] | Australia, 2022 | Survey | Journal of Medical Imaging and Radiation Oncology | Non-radiologists showed discomfort when acting on AI-generated medical reports. | ------------------------ |
Povyakalo et al. [22] | United Kingdom, 2013. | Randomized Control Trial | Medical Decision Making | ------------------------ | Improves performance of less-discriminating readers. --> “perception that implementation of a dynamic version of CAD (AI) will decrease errors in diagnosis”. |
Chen et al. [23] | UK, 2021 | Qualitative | BMC Health Services Research | ------------------------ | Radiologists believe AI has the potential to take on more repetitive tasks and allow them to focus on more interesting and challenging work. |
Alelyani et al. [24] | The Kingdom of Saudi Arabia, 2021 | Survey | Healthcare | ------------------------ | Eighty-two percent of the participants thought that AI must be included in the curriculum of medical and allied health colleges. |
Huisman et al. [25] | Africa/Europe/North America countries, 2021 | Survey | European Radiology | ------------------------ | Advanced knowledge of AI was inversely associated with the fear of implementation, |
Lee et al. [26] | United States, 2015 | Focus Group | American Journal of Radiology | Poor acceptance, negative perception of CDS. “Lack of agreement”. | Radiologists express a strong desire to be more involved in the implementation of CDS at their respective institutions. “Social influence positive user attitude”. |
van Hoek et al. [27] | Switzerland, 2019 | Survey | European Journal of Radiology | The majority of respondents agreed that AI should be implemented into radiology practice | Students decide against choosing radiology as a residency due to the future of AI in radiology |
Reeder et al. [28] | United States, 2022 | Survey | Clinical Imaging | AI makes medical diagnosis and Radiologists more efficient | Medical students fear the lack of job opportunities in Radiology due to AI |
Grimm et al. [29] | United States, 2022 | Mixed-Methods Study | Academic Radiology | Junior medial students held concerns about the limited job opportunities with AI integration | Medical students who have a positive view of AI and senior medical students do not express concerns regarding the scarcity of job opportunities resulting from the integration of AI in radiology. |
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Eltawil, F.A.; Atalla, M.; Boulos, E.; Amirabadi, A.; Tyrrell, P.N. Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review. Tomography 2023, 9, 1443-1455. https://doi.org/10.3390/tomography9040115
Eltawil FA, Atalla M, Boulos E, Amirabadi A, Tyrrell PN. Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review. Tomography. 2023; 9(4):1443-1455. https://doi.org/10.3390/tomography9040115
Chicago/Turabian StyleEltawil, Fatma A., Michael Atalla, Emily Boulos, Afsaneh Amirabadi, and Pascal N. Tyrrell. 2023. "Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review" Tomography 9, no. 4: 1443-1455. https://doi.org/10.3390/tomography9040115