The authors evaluated the influence of perceptual and cognitive skills in mammography detection and interpretation by testing three groups representing different levels of mammography expertise in terms of experience, training, and talent with a mammography screening-diagnostic task. One hundred fifty mammograms, composed of unilateral cranial-caudal and mediolateral oblique views, were displayed in pairs on a digital workstation to 19 radiology residents, three experienced mammographers, and nine mammography technologists. One-third of the mammograms showed malignant lesions; two-thirds were malignancy-free. Observers interacted with the display to indicate whether each image contained no malignant lesions or suspicious lesions indicating malignancy. Decision time was measured as the lesions were localized, classified, and rated for decision confidence. Compared with performance of experts, alternative free response operating characteristic performance for residents was significantly lower and equivalent to that of technologists. Analysis of overall performance showed that, as level of expertise decreased, false-positive results exerted a greater effect on overall decision accuracy over the time course of image perception. This defines the decision speed-accuracy relationship that characterizes mammography expertise. Differences in resident performance resulted primarily from lack of perceptual-learning experience during mammography training, which limited object recognition skills and made it difficult to determine differences between malignant lesions, benign lesions, and normal image perturbations. A proposed solution is systematic mentor-guided training that links image perception to feedback about the reasons underlying decision making.

OBJECTIVE. This article describes the time course of lesion detection on digital mammograms using data about both eye position and decision time to compare performance between experienced mammographers and trainees. Research indicates that a longer decision time works against performance in the interpretation of chest radiographs because the likelihood of error is increased, particularly for trainees. Is this relation between decision time and performance also true for interpreting mammograms? Is there an optimal decision time—performance trade-off for detecting breast lesions?MATERIALS AND METHODS. Six radiology trainees (experience, 302-976 cases) and three mammographers (experience, 3000-5000 cases per year) reviewed 40 test cases. Each test case was represented by two mammograms that showed different views of the same breast. Twenty breasts contained suspicious lesions, and 20 were lesion-free. An interactive computer display system with an eye—head tracker measured the timing of decisions, where visu...

The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.