The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

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Publication Details

Journal
Cognitive Research Principles and Implications
Published
2024-09-02
DOI
https://doi.org/10.1186/s41235-024-00576-4
Citations
3
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
1.04
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article

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search

Francesca Patterson, Melina A. Kunar
3 citations
Cognitive Research Principles and Implications
AI in cancer detection
1.04
article

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search

Francesca Patterson, Melina A. Kunar
article en
3 citations

Abstract

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

Cognitive Research Principles and ImplicationsVol. 9(1)
University of Warwick (GB)
Openalex Percentile: Top 19%
AI in cancer detection
1.04
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