How to discriminate between computer-aided and computer-hindered decisions: a case study in mammography.

Background: Computer aids can affect decisions in complex ways, potentially even making them worse; common assessment methods may miss these effects. We developed a method for estimating the quality of decisions, as well as how computer aids affect it, and applied it to computer-aided detection (CAD) of cancer, reanalyzing data from a published study where 50 professionals ("readers") interpreted 180 mammograms, both with and without computer support.

Method: We used stepwise regression to estimate how CAD affected the probability of a reader making a correct screening decision on a patient with cancer (sensitivity), thereby taking into account the effects of the difficulty of the cancer (proportion of readers who missed it) and the reader's discriminating ability (Youden's determinant).

Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography.

Rationale And Objectives: To investigate the effects of incorrect computer output on the reliability of the decisions of human users. This work followed an independent UK clinical trial that evaluated the impact of computer-aided detection(CAD) in breast screening. The aim was to use data from this trial to feed into probabilistic models (similar to those used in "reliability engineering") which would detect and assess possible ways of improving the human-CAD interaction.