Article Green Open Access 2017

The challenges of studying visual expertise in medical image diagnosis

Medical Education
Journal · Vol. 51 · Issue 1 · pp. 97-104
Abstract

Context: Visual expertise is the superior visual skill shown when executing domain-specific visual tasks. Understanding visual expertise is important in order to understand how the interpretation of medical images may be best learned and taught. In the context of this article, we focus on the visual skill of medical image diagnosis and, more specifically, on the methodological set-ups routinely used in visual expertise research. Methods: We offer a critique of commonly used methods and propose three challenges for future research to open up new avenues for studying characteristics of visual expertise in medical image diagnosis. The first challenge addresses theory development. Novel prospects in modelling visual expertise can emerge when we reflect on cognitive and socio-cultural epistemologies in visual expertise research, when we engage in statistical validations of existing theoretical assumptions and when we include social and socio-cultural processes in expertise development. The second challenge addresses the recording and analysis of longitudinal data. If we assume that the development of expertise is a long-term phenomenon, then it follows that future research can engage in advanced statistical modelling of longitudinal expertise data that extends the routine use of cross-sectional material through, for example, animations and dynamic visualisations of developmental data. The third challenge addresses the combination of methods. Alternatives to current practices can integrate qualitative and quantitative approaches in mixed-method designs, embrace relevant yet underused data sources and understand the need for multidisciplinary research teams. Conclusion: Embracing alternative epistemological and methodological approaches for studying visual expertise can lead to a more balanced and robust future for understanding superior visual skills in medical image diagnosis as well as other medical fields. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education

Keywords

Author Keywords

Not provided

Index Keywords

Skill learning clinical competence human medical education procedures Humans diagnosis knowledge theoretical model Education, Medical quantitative study validation process statistical model vision epistemology diagnostic imaging Visual Perception
Author Affiliations
Universiteit Maastricht, Maastricht, Limburg, Netherlands
Deggendorf Institute of Technology, Deggendorf, Bayern, Germany
Open Universiteit, Heerlen, Limburg, Netherlands
Queen’s University, Kingston, ON, Canada
Funding & Acknowledgements
No funding information
References 10 References
1 Bertram, Raymond, The Effect of Expertise on Eye Movement Behaviour in Medical Image Perception, PLoS ONE, 8, 6, (2013)
2 Gegenfurtner, Andreas, Transfer of expertise: An eye tracking and think aloud study using dynamic medical visualizations, Computers and Education, 63, pp. 393-403, (2013)
3 Balslev, Thomas, Visual expertise in paediatric neurology, European Journal of Paediatric Neurology, 16, 2, pp. 161-166, (2012)
4 Norman, Geoffrey R., Expertise in visual diagnosis: A review of the Literature, Academic Medicine, 67, 10, pp. S78-S83, (1992)
5 Wood, Beverly P., Visual expertise, Radiology, 211, 1, pp. 1-3, (1999)
6 Ericsson, Karl Anders, Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains, Academic Medicine, 79, 10 SUPPL., pp. S70-S81, (2004)
7 Cambridge Handbook of Expertise and Expert Performance, (2006)
8 Krupinski, Elizabeth A., Current perspectives in medical image perception, Attention, Perception, and Psychophysics, 72, 5, pp. 1205-1217, (2010)
9 Oxford Handbook on Eye Movements, (2011)
10 Gegenfurtner, Andreas, Expertise Differences in the Comprehension of Visualizations: A Meta-Analysis of Eye-Tracking Research in Professional Domains, Educational Psychology Review, 23, 4, pp. 523-552, (2011)
Quick Actions
Full Text via DOI
Citation Metrics
46
Times Cited (Scopus)

References 10
Document Identifiers