title: The challenges of studying visual expertise in medical image diagnosis.
abstract: BACKGROUND
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Technische Hochschule Deggendorf, Deggendorf, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Gegenfurtner
authors.affiliation: Maastricht University, Maastricht, the Netherlands.
authors.forename: Ellen
authors.initials: E
authors.lastname: Kok
authors.affiliation: Maastricht University, Maastricht, the Netherlands.
authors.forename: Koos
authors.initials: K
authors.lastname: van Geel
authors.affiliation: Maastricht University, Maastricht, the Netherlands.
authors.forename: Anique
authors.initials: A
authors.lastname: de Bruin
authors.affiliation: Open University of the Netherlands, Heerlen, The Netherlands.
authors.forename: Halszka
authors.initials: H
authors.lastname: Jarodzka
authors.affiliation: Queen's University, Kingston, Canada.
authors.forename: Adam
authors.identifier: http://orcid.org/0000-0002-3076-6221
authors.initials: A
authors.lastname: Szulewski
authors.affiliation: Maastricht University, Maastricht, the Netherlands.
authors.forename: Jeroen Jg
authors.initials: JJ
authors.lastname: van Merriënboer
mesh_terms: D002983:Clinical Competence; D003952:Diagnostic Imaging / Q000379:methods*; D004501:Education, Medical; D006801:Humans; D019359:Knowledge; D007858:Learning; D014796:Visual Perception