“Scanning errors” (also called “search errors”) result from failures in the first stage of search. Some authors have proposed that errors in medical image perception can be best understood as stemming from different aspects of the task-leading to three general types of errors: search, recognition, and decision errors ( Kundel, Nodine, & Carmody, 1978). Thus faulty perception is the most important source of interpretive error in diagnostic imaging ( Berlin, 2014 Donald & Barnard, 2012 Krupinski, 2010). Detection/omission errors account for 60% to 80% of interpretive errors ( Funaki, Szymski, & Rosenblum, 1997 Rosenkrantz & Bansal, 2016). This error rate is substantially higher-approximately 30%-when all images contain abnormalities ( Berlin, 2007 Rauschecker et al., 2020 see Waite et al., 2017 for a review). The diagnostic error rate in a typical clinical practice (comprising both normal and abnormal image studies) is between 3% and 4% ( Borgstede, Lewis, Bhargavan, & Sunshine, 2004 Siegle et al., 1998), which translates into approximately 40 million interpretive errors per year worldwide ( Bruno, Walker, & Abujudeh, 2015). 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 long-term goal of most studies of radiologist performance is to reduce error. Determining the precise features that radiologists use to discriminate abnormalities in medical images-and designing innovative heuristics for trainees that enable efficient learning of informative features-would help optimize performance in the field. The result is a knowledge base that has not translated into concrete methods of training derived from critical perceptual features. However, pattern recognition is difficult to teach ( Kellman & Garrigan, 2009), and expertise in viewing radiologic images is therefore gained largely as a function of the number of images read, rather than through explicit instruction and understanding ( Krupinski, Graham, & Weinstein, 2013 Nodine & Mello-Thoms, 2010). Mentors provide feedback about mistakes, guidance for what is benign versus malignant, and other conceptual, factual, and procedural information. That is to say, we do not precisely know what an expert radiologist does: currently, radiologists achieve peak expertise only after years of trial-and-error training, during which they acquire their skillset through veiled principles that are yet to be articulated. 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.Ĭurrent models of medical image perception are incomplete and demonstrate significant gaps in the current understanding of radiologic expertise (see Waite et al., 2019 for a review). 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. 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. 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. 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. 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. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images.
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