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Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli (e.g., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, ‘holistic’ mode of diagnostic processing is not contrasted with the slower ‘search-to-find’ mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the ‘holistic’ mode of diagnostic processing and less so to the ‘search-to-find’ mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode (i.e., during 2s trials), and less so in the search-to-find mode (i.e., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants’ expertise domain.

Rationale and Objectives Radiologists commonly use comparison films to improve their differential diagnosis. Educational literature suggests that this technique might also be used to bolster the process of learning to interpret radiographs. We investigated the effectiveness of three comparison techniques in medical students, whom we invited to compare cases of the same disease (same-disease comparison), cases of different diseases (different-disease comparison), disease images with normal images (disease/normal comparison), and identical images (no comparison/control condition). Furthermore, we used eye-tracking technology to investigate which elements of the two cases were compared by the students. Materials and Methods We randomly assigned 84 medical students to one of four conditions and had them study different diseases on chest radiographs, while their eye movements were being measured. Thereafter, participants took two tests that measured diagnostic performance and their ability to locate diseases, respectively. Results Students studied most efficiently in the same-disease and different-disease comparison conditions: test 1, F (3, 68) = 3.31, P = .025, η p 2 = 0.128; test 2, F (3, 65) = 2.88, P = .043, η p 2 = 0.117. We found that comparisons were effected in 91% of all trials (except for the control condition). Comparisons between normal anatomy were particularly common (45.8%) in all conditions. Conclusions Comparing cases can be an efficient way of learning to interpret radiographs, especially when the comparison technique used is specifically tailored to the learning goal. Eye tracking provided insight into the comparison process, by showing that few comparisons were made between abnormalities, for example.

ContextVisual 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.MethodsWe 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.ConclusionEmbracing 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.

To prevent radiologists from overlooking lesions, radiology textbooks recommend “systematic viewing,” a technique whereby anatomical areas are inspected in a fixed order. This would ensure complete inspection (full coverage) of the image and, in turn, improve diagnostic performance. To test this assumption, two experiments were performed. Both experiments investigated the relationship between systematic viewing, coverage, and diagnostic performance. Additionally, the first investigated whether systematic viewing increases with expertise; the second investigated whether novices benefit from full-coverage or systematic viewing training. In Experiment 1, 11 students, ten residents, and nine radiologists inspected five chest radiographs. Experiment 2 had 75 students undergo a training in either systematic, full-coverage (without being systematic) or non-systematic viewing. Eye movements and diagnostic performance were measured throughout both experiments. In Experiment 1, no significant correlations were found between systematic viewing and coverage, r = −.10, p = .62, and coverage and performance, r = −.06, p = .74. Experts were significantly more systematic than students F 2,25 = 4.35, p = .02. In Experiment 2, significant correlations were found between systematic viewing and coverage, r = −.35, p

Radiologists commonly use comparison films to improve their differential diagnosis. Educational literature suggests that this technique might also be used to bolster the process of learning to interpret radiographs. We investigated the effectiveness of three comparison techniques in medical students, whom we invited to compare cases of the same disease (same-disease comparison), cases of different diseases (different-disease comparison), disease images with normal images (disease/normal comparison), and identical images (no comparison/control condition). Furthermore, we used eye-tracking technology to investigate which elements of the two cases were compared by the students.We randomly assigned 84 medical students to one of four conditions and had them study different diseases on chest radiographs, while their eye movements were being measured. Thereafter, participants took two tests that measured diagnostic performance and their ability to locate diseases, respectively.Students studied most efficiently in the same-disease and different-disease comparison conditions: test 1, F(3, 68) = 3.31, P = .025, ηp(2) = 0.128; test 2, F(3, 65) = 2.88, P = .043, ηp(2) = 0.117. We found that comparisons were effected in 91% of all trials (except for the control condition). Comparisons between normal anatomy were particularly common (45.8%) in all conditions.Comparing cases can be an efficient way of learning to interpret radiographs, especially when the comparison technique used is specifically tailored to the learning goal. Eye tracking provided insight into the comparison process, by showing that few comparisons were made between abnormalities, for example.