Evidence map›Paper›PMID 39218972›Full record

ArticleCognitive research: principles and implications2024

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

Francesca Patterson, Melina A Kunar

Abstract read
In one paragraph

Article in Cognitive research: principles and implications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Intrinsic motivation and false feedback reduce the low prevalence effect.Cognitive research: principles and implications · 2025
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Francesca PattersonDepartment of Psychology, The University of Warwick, Coventry, CV4 7AL, UK. frankiepatterson16@gmail.com.ORCID 0009-0003-4000-5148
Melina A KunarDepartment of Psychology, The University of Warwick, Coventry, CV4 7AL, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Breast NeoplasmsMammographyAdultAgedCuesDiagnosis, Computer-AssistedEarly Detection of CancerFemaleHumansMiddle AgedArtificial intelligenceAutomationBinary CADComputer Aided Detection (CAD)Low prevalenceMammogramOver-reliance

Identifiers

PMID39218972
PMCPMC11366737

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.