Chest radiograph examples of local AI explanations and global AI explanations from a simulated AI tool (IMAGE)
Caption
Chest radiograph (CXR) examples of (A, C) local (feature-based) AI explanations and (B, D) global (prototype-based) AI explanations from a simulated AI tool, ChestAId, presented to physicians in the study. In all examples, the correct diagnostic impression for the radiograph case in question is “right upper lobe pneumonia,” and the corresponding AI advice is correct. The patient clinical information associated with this chest radiograph was “a 63-year-old male presenting to the Emergency Department with cough.” To better simulate a realistic AI system, explanation specificity was changed according to high (ie, 80%−94%) or low (ie, 65%–79%) AI confidence level: bounding boxes in high-confidence local AI explanations (example in A) were more precise than those in low-confidence ones (example in C); high-confidence global AI explanations (example in B) had more classic exemplar images than low-confidence ones (example in D), for which the exemplar images were more subtle.
Credit
Radiological Society of North America (RSNA)
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May use with credit.
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