An international survey on AI in radiology in 1041 radiologists and radiology residents

The paper summarises the results of an extensive survey answered by more than a thousand radiologists. The answers show that AI is expected to change the future of radiology within 10 years and it will most likely act as a second reader. Radiologists remain concerned about ethical and legal issues brought forward by this technology

An international survey on AI in radiology in 1041 radiologists and radiology residents part 2

Merel Huisman, Erik Ranschaert, William Parker, Domenico Mastrodicasa, Martin Koci, Daniel Pinto de Santos, Francesca Coppola, Sergey Morozov, Marc Zins, Cedric Bohyn, Ural Koç, Jie Wu, Satyam Veean, Dominik Fleischmann, Tim Leiner & Martin J. Willemink 

Background and purpose 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.

Read the full paper here:

Huisman, M., Ranschaert, E., Parker, W. et al. An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education. Eur Radiol (2021). https://doi.org/10.1007/s00330-021-07782-4

At Nicolab we believe connecting human & artificial intelligence will revolutionize emergency care. In an environment where time is so critical, using an AI solution to reduce the time to diagnosis and facilitate fast image exchange can really make every difference to a patients outcome as well as the radiologists workload. More education around the security, ethics and legal policies in the future would help to reduce hesitation around implementation.

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