An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education

Abstract

Objectives Currently, hurdles to implementation of artificial intelligence (AI) in radiology are a much-debated topic but have not been investigated in the community at large. Also, controversy exists if and to what extent AI should be incorporated into radiology residency programs. Methods Between April and July 2019, an international survey took place on AI regarding its impact on the profession and training. The survey was accessible for radiologists and residents and distributed through several radiological societies. Relationships of independent variables with opinions, hurdles, and education were assessed using multivariable logistic regression. Results The survey was completed by 1041 respondents from 54 countries. A majority (n = 855, 82%) expects that AI will cause a change to the radiology field within 10 years. Most frequently, expected roles of AI in clinical practice were second reader (n = 829, 78%) and work-flow optimization (n = 802, 77%). Ethical and legal issues (n = 630, 62%) and lack of knowledge (n = 584, 57%) were mentioned most often as hurdles to implementation. Expert respondents added lack of labelled images and generalizability issues. A majority (n = 819, 79%) indicated that AI should be incorporated in residency programs, while less support for imaging informatics and AI as a subspecialty was found (n = 241, 23%). Conclusions Broad community demand exists for incorporation of AI into residency programs. Based on the results of the current study, integration of AI education seems advisable for radiology residents, including issues related to data management, ethics, and legislation. Key Points • There is broad demand from the radiological community to incorporate AI into residency programs, but there is less support to recognize imaging informatics as a radiological subspecialty. • Ethical and legal issues and lack of knowledge are recognized as major bottlenecks for AI implementation by the radiological community, while the shortage in labeled data and IT-infrastructure issues are less often recognized as hurdles. • Integrating AI education in radiology curricula including technical aspects of data management, risk of bias, and ethical and legal issues may aid successful integration of AI into diagnostic radiology.

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

“…When asked about AI and specific terms the vast majority have either heard about AI or used it in daily work while only 2 of the 223 respondents have not heard about AI. This result is consistent with other similar surveys [ 16 – 20 ]. Other generic terms such as algorithm, ML or convolutional neural network were also familiar to the 50–75% of participants.…”

“…Every participant manifested the need to pursue academic training in new technologies and 92.9% think this should be implemented in the specialty’s academic program. This is also stated in other similar surveys [ 15 – 17 , 20 ].…”

“…The general lack of concern about job loss shown in our results is also shared in almost every survey [ 12 , 14 , 15 , 17 – 20 ], in which radiologists are rather keen to incorporate it in daily work. In the Italian survey, about 66% of the respondents defined AI as an aid to daily practice and believed that image interpretation will be handled by AI [ 12 ].…”

“…But perhaps the transformation will not be as disruptive as initially imagined, but more organic and intertwined with the daily workflow granting us to achieve a synergy between radiologist and computer, allowing radiologists to spend their time performing value-added functions and increase their professional satisfaction. This view is supported by some of the existing literature, where as many as 77% of respondents believe that workflow optimization will be possible by these tools [ 20 ]. In this regard, AI should be viewed as a wingman instead of a direct competitor and we should not be afraid of incorporating AI and view it as an opportunity to be in the vanguard as it might also have an impact in another medical fields.…”

“…There have also been surveys in the USA [ 16 ], Singapore [ 17 ] and Saudi Arabia [ 18 ]. Perhaps one of the most comprehensive studies in this regard so far is the two-part international survey conducted by Huisman et al [ 19 , 20 ] in 2019.…”