Evidence map›Paper›PMID 35723763›Full record

ArticleCognitive research: principles and implications2022

How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization.

Fallon Branch, K Matthew Williams, Isabella Noel Santana, Jay Hegdé

Abstract read
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Fallon Branch *Department of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, Augusta University, DNRM, CA-2003, 1469 Laney Walker Blvd, Augusta, GA, 30912-2697, USA.ORCID 0000-0001-9169-0796
K Matthew Williams *Department of Psychological Sciences, Augusta University, Augusta, GA, USA.
Isabella Noel SantanaDepartment of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, Augusta University, DNRM, CA-2003, 1469 Laney Walker Blvd, Augusta, GA, 30912-2697, USA.
Jay HegdéDepartment of Neuroscience and Regenerative Medicine, Medical College of Georgia, Augusta University, Augusta University, DNRM, CA-2003, 1469 Laney Walker Blvd, Augusta, GA, 30912-2697, USA. jhegde@augusta.edu.ORCID 0000-0002-5596-2878

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects' estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output.

Indexed as

Breast NeoplasmsMammographyComputersDiagnosis, Computer-AssistedEarly Detection of CancerFemaleHumansRadiologistsSensitivity and SpecificityTechnologyAssistive technologiesBase rate fallacyBase rate neglectComputer-assisted diagnosisMiss rate neglect

Identifiers

PMID35723763
PMCPMC9209598

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.