Floor Schuur1, Mohammad H Rezazade Mehrizi2, Erik Ranschaert3,4. 1. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 2. School of Business and Economics, KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, De Boelelaan 1105, VU Main Building A-wing, 5th floor, 1081 HV, Amsterdam, The Netherlands. m.rezazademehrizi@vu.nl. 3. Department of Radiology, Elisabeth-Tweesteden Hospital (ETZ), Tilburg, The Netherlands. 4. Ghent University, Ghent, Belgium.
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.
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.
Authors: D Pinto Dos Santos; D Giese; S Brodehl; S H Chon; W Staab; R Kleinert; D Maintz; B Baeßler Journal: Eur Radiol Date: 2018-07-06 Impact factor: 5.315
Authors: Bo Gong; James P Nugent; William Guest; William Parker; Paul J Chang; Faisal Khosa; Savvas Nicolaou Journal: Acad Radiol Date: 2018-11-11 Impact factor: 3.173