A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence

Waite, Stephen; Farooq, Zerwa; Grigorian, Arkadij; Sistrom, Chris L.; Kolla, Srinivas; Mancuso, Anthony A.; Martínez-Conde, Susana; Alexander, Robert G.; Kantor, Alan; Macknik, Stephen L.

doi:10.1016/j.acra.2019.08.018

Cited by 31 publications

(22 citation statements)

References 98 publications

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“…Here, we consider these issues in the context of a real-world domain that, at least on the surface, appears to be well suited for the use of perceptual learning techniques to enhance current practice-learning to make radiological diagnoses (Kellman, 2013;Kelly, Rainford, McEntee, & Kavanagh, 2018;Kundel & Nodine, 1983;Li, Toh, Remington, & Jiang, 2020;Sowden, Davies, & Roling, 2000;Waite et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Perceptual Learning of Appendicitis Diagnosis in Radiological Images

Johnston

Cochrane

et al. 2020

Journal of Vision

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A sizeable body of work has demonstrated that participants have the capacity to show substantial increases in performance on perceptual tasks given appropriate practice. This has resulted in significant interest in the use of such perceptual learning techniques to positively impact performance in real-world domains where the extraction of perceptual information in the service of guiding decisions is at a premium. Radiological training is one clear example of such a domain. Here we examine a number of basic science questions related to the use of perceptual learning techniques in the context of a radiology-inspired task. On each trial of this task, participants were presented with a single axial slice from a CT image of the abdomen. They were then asked to indicate whether or not the image was consistent with appendicitis. We first demonstrate that, although the task differs in many ways from standard radiological practice, it nonetheless makes use of expert knowledge, as trained radiologists who underwent the task showed high (near ceiling) levels of performance. Then, in a series of four studies we show that (1) performance on this task does improve significantly over a reasonably short period of training (on the scale of a few hours); (2) the learning transfers to previously unseen images and to untrained image orientations; (3) purely correct/incorrect feedback produces weak learning compared to more informative feedback where the spatial position of the appendix is indicated in each image; and (4) there was little benefit seen from purposefully structuring the learning experience by starting with easier images and then moving on to more difficulty images (as compared to simply presenting all images in a random order). The implications for these various findings with respect to the use of perceptual learning techniques as part of radiological training are then discussed.

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Section: Introductionmentioning

confidence: 99%

Perceptual Learning of Appendicitis Diagnosis in Radiological Images

Johnston

Cochrane

et al. 2020

Journal of Vision

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“…When intermediates are compared to novices and experts, the picture becomes less black and white. Average fixation durations and time-to-first fixation on abnormalities were different from the hypotheses (27,51):…”

Section: Mapping Expertise Development In Intermediatescontrasting

confidence: 62%

“…One step further, artificial intelligence could eventually result in augmented radiology training. Augmented radiology training is analogous to future augmented radiology practice, where computers will support humans to produce the most fruitful results (51,52). Augmented radiology training could consist of an automated feedback system based on the eye-tracking and outcome measures, and it could also help in the selection of cases from an image bank to make the training adaptive to individual learner's needs.…”

Section: Practical Implicationsmentioning

confidence: 99%

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Lifelong learning in radiology

Geel¹

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“…Perceptual errors in radiology are a significant contributor to patient harm (Waite et al, 2019;Waite et al, 2017). Educational and practical interventions to improve human perceptual and decision-making skills are therefore needed to improve diagnostic accuracy and to reduce medical error (Ekpo, Alakhras, & Brennan, 2018;Waite et al, 2020;Waite et al, 2019). However, the features used by expert radiologists during visual inspection of medical images are not yet well specified.…”

Section: Discussionmentioning

confidence: 99%

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

et al. 2020

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Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health. Error rates in radiology The long-term goal of most studies of radiologist performance is to reduce error. Although it is difficult to determine the precise error rates in current clinical practice, it is clear that reductions in error rate would improve patient care. The diagnostic error rate in a typical clinical practice (comprising both normal

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A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence

Cited by 31 publications

References 98 publications

Perceptual Learning of Appendicitis Diagnosis in Radiological Images

Perceptual Learning of Appendicitis Diagnosis in Radiological Images

Lifelong learning in radiology

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

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