Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography
Open accessAbstract
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. Some analyses required extra data; therefore, two supplementary studies were conducted. Study 1 was designed to elucidate the effects of computer failure on human performance. Study 2 was conducted to clarify unexpected findings from Study 1.Materials and methods In Study 1, 20 film readers viewed 60 sets of mammograms (30 of which contained cancer) and provided "recall/no recall" decisions for each case. Computer output for each case was available to the participants. The test set was designed to contain an unusually large proportion (50%) of cancers for which CAD had generated incorrect output. In Study 2, 19 different readers viewed the same set of cases in similar conditions except that computer output was not available.Results The average sensitivity of readers in Study 1 (with CAD) was significantly lower than the average sensitivity of read-ers in Study 2 (without CAD). The difference was most marked for cancers for which CAD failed to provide correct prompting.Conclusion Possible automation bias effects in CAD use deserve further study because they may degrade human decision-making for some categories of cases under certain conditions. This possibility should be taken into account in the assessment and design of CAD tools.
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If physicians do not trust the technology, they will not use it, but blind trust in the technology can lead to medical errors.
“If physicians do not trust the technology, they will not use it, but blind trust in the technology can lead to medical errors8–11.”
From Do as AI say: susceptibility in deployment of clinical decision-aids (2021) · cites this paper as [11]
The Discussion (page not available in our copy) describes readers treating missing CAD prompts as reassurance and potentially missing cancers—support for the warning about overreliance. It does not address whether physicians who distrust a technology will decline to use it; that part of the attributed claim is unsupported. (LLM, conf=0.87)
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“There have been a small number of studies conducted in healthcare, finding evidence of AB omission errors in computer-aided detection of cancers in mammograms, [15, 16] and”
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- Authors
- Alberdi, E., Povyakalo, A., Strigini, L., & Ayton, P.
- Affiliations
- City, University of London (GB)
- Corresponding author
- Alberdi, Eugenio
- Venue
- Academic Radiology
- Publisher
- Elsevier BV
- Published
- 2004-08
- Volume
- 11
- Issue
- 8
- Pages
- 909-918
- ISSN
- 1076-6332 (ISSN-L)
- OpenAlex
- W2155859548
- Cited by
- 124
- References
- 11
- Citations by year
- 2026: 10 · 2025: 7 · 2024: 17 · 2023: 9 · 2022: 10 · 2021: 6 · 2020: 8 · 2019: 8 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 3 · 2014: 3 · 2013: 2 · 2012: 5
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- Locations
- Academic Radiology (publishedVersion)
- PubMed (publishedVersion)
- City Research Online (City University London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- computer-aided detection, mammography, breast cancer screening, human decision-making, automation bias, reader sensitivity, computer system failures, probabilistic reliability model
- MeSH terms
- Analysis of Variance, Breast Neoplasms, Decision Making, Diagnosis, Computer-Assisted, False Negative Reactions, Female, Follow-Up Studies, Humans, Image Processing, Computer-Assisted, Mammography, Mass Screening, Probability, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Reproducibility of Results, Models, Statistical, Bias
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