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The study of human perception is as old as medical imaging. Understanding perception has yielded the rules of engagement for radiologists as they tackle the “Where’s Waldo?” situations, the satisfaction of search problem, distractions, fatigue, the varying subtlties of disease states and normal, their prior training and experience, and the somewhat endless non-image-interpretation tasks associated with a radiology practice. The understanding of artificial intelligence (AI) on a radiologist’s interpretation can be likened to considering the suggestions from a first-year resident to incorporating insights from a seasoned expert. Kundel’s eye gaze experiments which demonstrated the search patterns of radiologists and laymen continue to be used today to understand the added influence of AI in the end user’s performance. Multi-disciplinary perception research has evolved from understanding human performance in the interpretation of medical images, to the understanding of computer-aided diagnosis (CAD), and to now the understanding of AI -- either as an aid to radiologists as a second reader, a concurrent reader, or a primary reader, or as a complete replacement. This lecture will take the audience through history to appreciate the role and necessity of perception (and its associated metrics of performance) in the development, validation, and ultimate future implementation of AI in the clinical radiology workflow.
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