Framing the fallibility of Computer-Aided Detection aids cancer detection

Abstract

Abstract Computer-Aided Detection (CAD) has been proposed to help operators search for cancers in mammograms. Previous studies have found that although accurate CAD leads to an improvement in cancer detection, inaccurate CAD leads to an increase in both missed cancers and false alarms. This is known as the over-reliance effect. We investigated whether providing framing statements of CAD fallibility could keep the benefits of CAD while reducing over-reliance. In Experiment 1, participants were told about the benefits or costs of CAD, prior to the experiment. Experiment 2 was similar, except that participants were given a stronger warning and instruction set in relation to the costs of CAD. The results showed that although there was no effect of framing in Experiment 1, a stronger message in Experiment 2 led to a reduction in the over-reliance effect. A similar result was found in Experiment 3 where the target had a lower prevalence. The results show that although the presence of CAD can result in over-reliance on the technology, these effects can be mitigated by framing and instruction sets in relation to CAD fallibility.

Keywords

• Mammogram
• Artificial Intelligence
• Visual search
• Computer-Aided Detection (CAD)
• Over-reliance
• Framing