ArticleCognitive research: principles and implications2023
Framing the fallibility of Computer-Aided Detection aids cancer detection.
Article in Cognitive research: principles and implications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07158203 (Assistance for Optical Diagnosis of Neoplasia Using Artificial Intelligence), which is not on this map. Cited by 7 papers.
What it found
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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.
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.
Assistance for Optical Diagnosis of Neoplasia Using Artificial Intelligence (FAIR Study)
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Trial
- Psychophysiological markers of trust in automation: insights from ERP responses in a modified flanker task.Cognitive research: principles and implications · 2026Article
- Intrinsic motivation and false feedback reduce the low prevalence effect.Cognitive research: principles and implications · 2025Article
- Increasing transparency of computer-aided detection impairs decision-making in visual search.Psychonomic bulletin & review · 2025Article
- The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search.Cognitive research: principles and implications · 2024Article
- Cue relevance drives early quitting in visual search.Cognitive research: principles and implications · 2024Article
- Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.Cognitive research: principles and implications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
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What OpenQuestion holds
Registered trials
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.