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Published in: Insights into Imaging 1/2019

Open Access 01-12-2019 | Artificial Intelligence | Educational Review

How scientific mobility can help current and future radiology research: a radiology trainee’s perspective

Author: Filippo Pesapane

Published in: Insights into Imaging | Issue 1/2019

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Abstract

One of the ways in which modern radiology is manifesting itself in higher education and research is through the increasing importance of scientific mobility. This article seeks to provide an overview and a prospective of radiology fellows in their last year of training about the current trends and policy tools for promoting mobility among young radiologists, especially inside the European Union. Nowadays, the need to promote international cooperation is even greater to ensure that the best evidence-based medical practices become a common background of a next cross-border generation of radiologists. Organisations such as the European Society of Radiology (ESR) and the Radiological Society of North America (RSNA) are called upon to play as guarantors of the training of young radiologists building know-how and world-class excellence. Today, it is not just being certified radiologist that matters, the place where the training was done plays an important role in enhancing chances when applying for a high-level job or fellowship. The article argues that the mobility of radiology trainees is an indispensable prerequisite to face new challenges, including the application of artificial intelligence to medical imaging, which will require a large multicentre collaboration.
Literature
1.
go back to reference Bilecen B, Van Mol C (2017) Introduction: international academic mobility and inequalities. J Ethn Migr Stud 43:1241–1255CrossRef Bilecen B, Van Mol C (2017) Introduction: international academic mobility and inequalities. J Ethn Migr Stud 43:1241–1255CrossRef
2.
go back to reference Kim T (2017) Academic mobility, transnational identity capital, and stratification under conditions of academic capitalism. High Educ 1:1–17 Kim T (2017) Academic mobility, transnational identity capital, and stratification under conditions of academic capitalism. High Educ 1:1–17
3.
go back to reference Jacobone V, Moro G (2015) Evaluating the impact of the Erasmus programme: skills and European identity. Assessment & Evaluation in Higher Education 40:309–328CrossRef Jacobone V, Moro G (2015) Evaluating the impact of the Erasmus programme: skills and European identity. Assessment & Evaluation in Higher Education 40:309–328CrossRef
4.
go back to reference European Commission (2019) Science, Research and Innovation Performance of the EU 2018. European Commission (2019) Science, Research and Innovation Performance of the EU 2018.
5.
go back to reference European Commission (2013) DG Research and Innovation. Researchers’ Report 2013. Final Report, European Commission (2013) DG Research and Innovation. Researchers’ Report 2013. Final Report,
6.
go back to reference Van der Wende M (2015) International academic mobility: towards a concentration of the minds in. Eur Rev 23:S70–S88CrossRef Van der Wende M (2015) International academic mobility: towards a concentration of the minds in. Eur Rev 23:S70–S88CrossRef
7.
go back to reference Collins J (2006) Medical education research: challenges and opportunities. Radiology 240:639–647CrossRef Collins J (2006) Medical education research: challenges and opportunities. Radiology 240:639–647CrossRef
8.
go back to reference Jacob M, Meek VL (2013) Scientific mobility and international research networks: trends and policy tools for promoting research excellence and capacity building. Stud High Educ 38:331–344CrossRef Jacob M, Meek VL (2013) Scientific mobility and international research networks: trends and policy tools for promoting research excellence and capacity building. Stud High Educ 38:331–344CrossRef
12.
go back to reference Shapiro S, Coleman EA, Broeders M et al (1998) Breast cancer screening programmes in 22 countries: current policies, administration and guidelines. International Breast Cancer Screening Network (IBSN) and the European Network of Pilot Projects for Breast Cancer Screening. Int J Epidemiol 27:735–742CrossRef Shapiro S, Coleman EA, Broeders M et al (1998) Breast cancer screening programmes in 22 countries: current policies, administration and guidelines. International Breast Cancer Screening Network (IBSN) and the European Network of Pilot Projects for Breast Cancer Screening. Int J Epidemiol 27:735–742CrossRef
13.
go back to reference Larson DB, Towbin AJ, Pryor RM, Donnelly LF (2013) Improving consistency in radiology reporting through the use of department-wide standardized structured reporting. Radiology 267:240–250CrossRef Larson DB, Towbin AJ, Pryor RM, Donnelly LF (2013) Improving consistency in radiology reporting through the use of department-wide standardized structured reporting. Radiology 267:240–250CrossRef
14.
go back to reference Leong S, Keeling AN, Lee MJ (2009) A survey of interventional radiology awareness among final-year medical students in a European country. Cardiovasc Intervent Radiol 32:623–629CrossRef Leong S, Keeling AN, Lee MJ (2009) A survey of interventional radiology awareness among final-year medical students in a European country. Cardiovasc Intervent Radiol 32:623–629CrossRef
15.
go back to reference Hamoen EHJ, de Rooij M, Witjes JA, Barentsz JO, Rovers MM (2015) Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol 67:1112–1121CrossRef Hamoen EHJ, de Rooij M, Witjes JA, Barentsz JO, Rovers MM (2015) Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol 67:1112–1121CrossRef
17.
go back to reference Swensen SJ, Johnson CD (2005) Radiologic quality and safety: mapping value into radiology. J Am Coll Radiol 2:992–1000CrossRef Swensen SJ, Johnson CD (2005) Radiologic quality and safety: mapping value into radiology. J Am Coll Radiol 2:992–1000CrossRef
18.
go back to reference Schwartz LH, Panicek DM, Berk AR, Li Y, Hricak H (2011) Improving communication of diagnostic radiology findings through structured reporting. Radiology 260:174–181CrossRef Schwartz LH, Panicek DM, Berk AR, Li Y, Hricak H (2011) Improving communication of diagnostic radiology findings through structured reporting. Radiology 260:174–181CrossRef
19.
go back to reference Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI Images. IEEE Trans Med Imaging 35:1240–1251CrossRef Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI Images. IEEE Trans Med Imaging 35:1240–1251CrossRef
20.
go back to reference Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Isgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35:1252–1261CrossRef Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Isgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35:1252–1261CrossRef
21.
go back to reference Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2:35CrossRef Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2:35CrossRef
22.
go back to reference Langlotz CP, Caldwell SA (2002) The completeness of existing lexicons for representing radiology report information. J Digit Imaging 15(Suppl 1):201–205CrossRef Langlotz CP, Caldwell SA (2002) The completeness of existing lexicons for representing radiology report information. J Digit Imaging 15(Suppl 1):201–205CrossRef
23.
go back to reference Reiner BI, Knight N, Siegel EL (2007) Radiology reporting, past, present, and future: the radiologist's perspective. J Am Coll Radiol 4:313–319CrossRef Reiner BI, Knight N, Siegel EL (2007) Radiology reporting, past, present, and future: the radiologist's perspective. J Am Coll Radiol 4:313–319CrossRef
27.
go back to reference Kahn CE Jr, Heilbrun ME, Applegate KE (2013) From guidelines to practice: how reporting templates promote the use of radiology practice guidelines. Journal of the American College of Radiology 10:268–273CrossRef Kahn CE Jr, Heilbrun ME, Applegate KE (2013) From guidelines to practice: how reporting templates promote the use of radiology practice guidelines. Journal of the American College of Radiology 10:268–273CrossRef
28.
go back to reference Sardanelli F, Hunink MG, Gilbert FJ, Di Leo G, Krestin GP (2010) Evidence-based radiology: why and how? Eur Radiol 20:1–15CrossRef Sardanelli F, Hunink MG, Gilbert FJ, Di Leo G, Krestin GP (2010) Evidence-based radiology: why and how? Eur Radiol 20:1–15CrossRef
29.
30.
go back to reference Kemp JL, Mahoney MC, Mathews VP, Wintermark M, Yee J, Brown SD (2017) Patient-centered radiology: where are we, where do we want to be, and how do we get there? Radiology 285:601–608CrossRef Kemp JL, Mahoney MC, Mathews VP, Wintermark M, Yee J, Brown SD (2017) Patient-centered radiology: where are we, where do we want to be, and how do we get there? Radiology 285:601–608CrossRef
31.
go back to reference European Society of Radiology (2009) The future role of radiology in healthcare. Insights Imaging 1:2–11CrossRef European Society of Radiology (2009) The future role of radiology in healthcare. Insights Imaging 1:2–11CrossRef
32.
go back to reference Mohan C SM (2018) Artificial intelligence in radiology—are we treating the image or the patient? Indian J Radiol Imaging 28:137–139CrossRef Mohan C SM (2018) Artificial intelligence in radiology—are we treating the image or the patient? Indian J Radiol Imaging 28:137–139CrossRef
33.
go back to reference Thrall JH, Li X, Li Q et al (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology 15:504–508CrossRef Thrall JH, Li X, Li Q et al (2018) Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology 15:504–508CrossRef
35.
go back to reference Miller DD, Brown EW (2018) Artificial intelligence in medical practice: the question to the answer? Am J Med 131:129–133CrossRef Miller DD, Brown EW (2018) Artificial intelligence in medical practice: the question to the answer? Am J Med 131:129–133CrossRef
36.
go back to reference Krittanawong C (2018) The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med 48:e13–e14CrossRef Krittanawong C (2018) The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med 48:e13–e14CrossRef
37.
go back to reference Lee JG, Jun S, Cho YW et al (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18:570–584CrossRef Lee JG, Jun S, Cho YW et al (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18:570–584CrossRef
38.
go back to reference Kruskal JB, Berkowitz S, Geis JR, Kim W, Nagy P, Dreyer K (2017) Big data and machine learning-strategies for driving this bus: a summary of the 2016 Intersociety Summer Conference. Journal of the American College of Radiology 14:811–817CrossRef Kruskal JB, Berkowitz S, Geis JR, Kim W, Nagy P, Dreyer K (2017) Big data and machine learning-strategies for driving this bus: a summary of the 2016 Intersociety Summer Conference. Journal of the American College of Radiology 14:811–817CrossRef
39.
go back to reference Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef
40.
go back to reference Jha S, Topol EJ (2016) Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316:2353–2354CrossRef Jha S, Topol EJ (2016) Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316:2353–2354CrossRef
42.
go back to reference Lakhani P, Prater AB, Hutson RK et al (2018) Machine learning in radiology: applications beyond image interpretation. Journal of the American College of Radiology 15:350–359CrossRef Lakhani P, Prater AB, Hutson RK et al (2018) Machine learning in radiology: applications beyond image interpretation. Journal of the American College of Radiology 15:350–359CrossRef
43.
go back to reference King BF Jr (2017) Guest editorial: discovery and artificial intelligence. AJR Am J Roentgenol 209:1189–1190CrossRef King BF Jr (2017) Guest editorial: discovery and artificial intelligence. AJR Am J Roentgenol 209:1189–1190CrossRef
44.
go back to reference Recht M, Bryan RN (2017) Artificial intelligence: threat or boon to radiologists? Journal of the American College of Radiology 14:1476–1480CrossRef Recht M, Bryan RN (2017) Artificial intelligence: threat or boon to radiologists? Journal of the American College of Radiology 14:1476–1480CrossRef
45.
go back to reference European Board of Radiology (EBR) (2018) The European Diploma in Radiology (EDiR): investing in the future of the new generations of radiologists. Insights Imaging 9:905–909 European Board of Radiology (EBR) (2018) The European Diploma in Radiology (EDiR): investing in the future of the new generations of radiologists. Insights Imaging 9:905–909
46.
go back to reference European Society of Radiology (ESR) (2018) Radiology trainees forum survey report on workplace satisfaction, ESR education, mobility and stress level. Insights Imaging 9:755–759CrossRef European Society of Radiology (ESR) (2018) Radiology trainees forum survey report on workplace satisfaction, ESR education, mobility and stress level. Insights Imaging 9:755–759CrossRef
47.
go back to reference Nyhsen CM, Lawson C, Higginson J (2011) Radiology teaching for junior doctors: their expectations, preferences and suggestions for improvement. Insights Imaging 2:261–266CrossRef Nyhsen CM, Lawson C, Higginson J (2011) Radiology teaching for junior doctors: their expectations, preferences and suggestions for improvement. Insights Imaging 2:261–266CrossRef
48.
go back to reference Chew FS (1990) Standardization of the curriculum for resident education in diagnostic radiology. Invest Radiol 25:1258–1260CrossRef Chew FS (1990) Standardization of the curriculum for resident education in diagnostic radiology. Invest Radiol 25:1258–1260CrossRef
50.
go back to reference Eger MA, Valdez S (2018) From radical right to neo-nationalist. Eur Political Sci 1:1–21 Eger MA, Valdez S (2018) From radical right to neo-nationalist. Eur Political Sci 1:1–21
51.
go back to reference Eurostat (2019) Unemployment rates, seasonally adjusted, December 2018, Eurostat (2019) Unemployment rates, seasonally adjusted, December 2018,
52.
go back to reference Weiß A (2016) Understanding physicians’ professional knowledge and practice in research on skilled migration. Ethn Health 21:397–409CrossRef Weiß A (2016) Understanding physicians’ professional knowledge and practice in research on skilled migration. Ethn Health 21:397–409CrossRef
Metadata
Title
How scientific mobility can help current and future radiology research: a radiology trainee’s perspective
Author
Filippo Pesapane
Publication date
01-12-2019
Publisher
Springer Berlin Heidelberg
Published in
Insights into Imaging / Issue 1/2019
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-019-0773-z

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