ArticleCognitive research: principles and implications2022
The optimal use of computer aided detection to find low prevalence cancers.
Article in Cognitive research: principles and implications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Artificial intelligence and computer-aided diagnosis in diagnostic decisions: 5 questions for medical informatics and human-computer interface research.Journal of the American Medical Informatics Association : JAMIA · 2026Review
- Intrinsic motivation and false feedback reduce the low prevalence effect.Cognitive research: principles and implications · 2025Article
- Empirically derived evaluation requirements for responsible deployments of AI in safety-critical settings.NPJ digital medicine · 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
- Framing the fallibility of Computer-Aided Detection aids cancer detection.Cognitive research: principles and implications · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
People miss a high proportion of targets that only appear rarely. This low prevalence (LP) effect has implications for applied search tasks such as the clinical reading of mammograms. Computer aided detection (CAD) has been used to help radiologists search mammograms by highlighting areas likely to contain a cancer. Previous research has found a benefit in search when CAD cues were correct but a cost to search when CAD cues were incorrect. The current research investigated whether there is an optimal way to present CAD to ensure low error rates when CAD is both correct and incorrect. Experiment 1 compared an automatic condition, where CAD appeared simultaneously with the display to an interactive condition, where participants could choose to use CAD. Experiment 2 compared the automatic condition to a confirm condition, where participants searched the display first before being shown the CAD cues. The results showed that miss errors were reduced overall in the confirm condition, with no cost to false alarms. Furthermore, having CAD be interactive, resulted in a low uptake where it was only used in 34% of trials. The results showed that the presentation mode of CAD can affect decision-making in LP search.
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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.