A Review of Research into the Development of Radiologic Expertise: Implications for Computer-based Training
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Rationale And Objectives: Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.
Materials And Methods: This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.
Results And Conclusion: Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions.
What do experts look at and what do experts find when reading mammograms?.
Wolfe J, Wu C, Li J, Suresh S J Med Imaging (Bellingham). 2021; 8(4):045501.
PMID: 34277890 PMC: 8277193. DOI: 10.1117/1.JMI.8.4.045501.
Training focal lung pathology detection using an eye movement modeling example.
Brams S, Ziv G, Hooge I, Levin O, Verschakelen J, Williams A J Med Imaging (Bellingham). 2021; 8(2):025501.
PMID: 33732754 PMC: 7955141. DOI: 10.1117/1.JMI.8.2.025501.
A think-aloud study to inform the design of radiograph interpretation practice.
Yoon J, Boutis K, Pecaric M, Fefferman N, Ericsson K, Pusic M Adv Health Sci Educ Theory Pract. 2020; 25(4):877-903.
PMID: 32140874 PMC: 7471179. DOI: 10.1007/s10459-020-09963-0.
Analysis of Perceptual Expertise in Radiology - Current Knowledge and a New Perspective.
Waite S, Grigorian A, Alexander R, Macknik S, Carrasco M, Heeger D Front Hum Neurosci. 2019; 13:213.
PMID: 31293407 PMC: 6603246. DOI: 10.3389/fnhum.2019.00213.
Computer-based self-training for CT colonography with and without CAD.
Sali L, Delsanto S, Sacchetto D, Correale L, Falchini M, Ferraris A Eur Radiol. 2018; 28(11):4783-4791.
PMID: 29796918 DOI: 10.1007/s00330-018-5480-5.