Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review

Abstract

Objectives Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. Methods A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. Results Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. Conclusions Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications.

Keywords

Radiology; Artificial intelligence; Attitude; Education; Ethics

Published in

European Radiology
2022, volume: 32, number: 3, pages: 1477-1495

SLU Authors

• Belaghi, Reza

  • University of Tabriz

UKÄ Subject classification

Radiology, Nuclear Medicine and Medical Imaging
Sociology (excluding Social work, Social Psychology and Social Anthropology)
Computer Science

Publication identifier

• DOI: https://doi.org/10.1007/s00330-021-08214-z

Permanent link to this page (URI)

https://res.slu.se/id/publ/126952