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European Radiology

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Open Access 01-08-2021 | Artificial Intelligence | Imaging Informatics and Artificial Intelligence

Training opportunities of artificial intelligence (AI) in radiology: a systematic review

Authors: Floor Schuur, Mohammad H. Rezazade Mehrizi, Erik Ranschaert

Published in: European Radiology | Issue 8/2021

Abstract

Objectives

The aim is to offer an overview of the existing training programs and critically examine them and suggest avenues for further development of AI training programs for radiologists.

Methods

Deductive thematic analysis of 100 training programs offered in 2019 and 2020 (until June 30). We analyze the public data about the training programs based on their “contents,” “target audience,” “instructors and offering agents,” and “legitimization strategies.”

Results

There are many AI training programs offered to radiologists, yet most of them (80%) are short, stand-alone sessions, which are not part of a longer-term learning trajectory. The training programs mainly (around 85%) focus on the basic concepts of AI and are offered in passive mode. Professional institutions and commercial companies are active in offering the programs (91%), though academic institutes are limitedly involved.

Conclusions

There is a need to further develop systematic training programs that are pedagogically integrated into radiology curriculum. Future training programs need to further focus on learning how to work with AI at work and be further specialized and customized to the contexts of radiology work.

Key Points

• Most of AI training programs are short, stand-alone sessions, which focus on the basics of AI.

• The content of training programs focuses on medical and technical topics; managerial, legal, and ethical topics are marginally addressed.

• Professional institutions and commercial companies are active in offering AI training; academic institutes are limitedly involved.

Available only for authorised users

Curtis Langlotz, posted on Twitter on Feb 8, 2017

The study is not yet published: 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

https://www.myesr.org/education/training-curricula

https://siim.org/page/niic

Formal accreditation is an important motivation for radiologists who need to collect a series of credits/points to be able to renew their professional eligibility.

See NIIC-RAD program as an exception.

See the online training on “Fundamentals of Machine Learning for Healthcare,” from Stanford Uni, as an example of training programs developed with regard to radiologists’ needs.

Gong B, Nugent JP, Guest W et al (2019) Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: anational survey study. Acad Radiol 26:566–577. https://doi.org/10.1016/j.acra.2018.10.007CrossRefPubMed

Pinto dos Santos D, Giese D, Brodehl S et al (2019) Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol 29:1640–1646. https://doi.org/10.1007/s00330-018-5601-1CrossRefPubMed

European Society of Radiology (ESR) (2019) What the radiologist should know about artificial intelligence-an ESR white paper. Insights Imaging 10:44. https://doi.org/10.1186/s13244-019-0738-2

Collado-Mesa F, Alvarez E, Arheart K (2018) The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol 15:1753–1757. https://doi.org/10.1016/j.jacr.2017.12.021CrossRefPubMed

Nguyen GK, Shetty AS (2018) Artificial intelligence and machine learning: opportunities for radiologists in training. J Am Coll Radiol 15:1320–1321. https://doi.org/10.1016/j.jacr.2018.05.024CrossRefPubMed

Kotter E, Ranschaert E (2020) Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow. Eur Radiol. https://doi.org/10.1007/s00330-020-07148-2

Winter DG (1992) Content analysis of archival materials, personal documents, and everyday verbal productions. In: Smith CP, Atkinson JW, McClelland DC, Veroff J (eds) Motivation and personality: handbook of thematic content analysis. Cambridge University, New York

Boyatzis RE (1998) Transforming qualitative information: thematic analysis and code development. Sage Publications, Thousand Oaks, CA

Miles MB, Huberman AM, Saldana J (2013) Qualitative data analysis. Sage

10.

Ranschaert ER, Van Ooijen PMA, McGinty GB, Parizel PM (2016) Radiologists’ usage of social media: results of the RANSOM survey. J Digit Imaging 29:443–449. https://doi.org/10.1007/s10278-016-9865-1CrossRefPubMedPubMedCentral

11.

Clarke CGD, Nnajiuba U, Howie J, Khan M, Pinto Dos Santos D, Ranschaert E (2020) Giving radiologists a voice: a review of podcasts in radiology. Insights Imaging 11:33. https://doi.org/10.1186/s13244-020-0842-3

Title: Training opportunities of artificial intelligence (AI) in radiology: a systematic review
Authors: Floor Schuur
Mohammad H. Rezazade Mehrizi
Erik Ranschaert
Publication date: 01-08-2021
Publisher: Springer Berlin Heidelberg
Keyword: Artificial Intelligence
Published in: European Radiology / Issue 8/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07621-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Training opportunities of artificial intelligence (AI) in radiology: a systematic review

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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