Evidence map›Paper›PMID 41498825›Full record

ArticleEuropean radiology2026

How much radiologist time can be saved by implementing AI in screen-reading mammograms?

Tone Hovda, Åsne S Holen, Solveig Hofvind

Abstract read
In one paragraph

Article in European radiology, 2026. 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

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2 · The registry

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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

3 authors.

Tone HovdaDepartment of Radiology, Vestre Viken Hospital Trust, Drammen, Norway.
Åsne S HolenDepartment of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway.
Solveig HofvindDepartment of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway. sshh@fhi.no.ORCID http://orcid.org/0000-0003-0178-8939

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThere is a lack of breast radiologists in Norway and in Europe. Artificial intelligence (AI) offers an alternative to solely human readers and has demonstrated promising results in cancer detection in mammographic screening. We aimed to estimate the potential reduction in radiologists' workload by replacing one of the two radiologists with AI in screen-reading mammograms in BreastScreen Norway. MATERIALS AND

methodsBreastScreen Norway targets about 680,000 women aged 50-69 who are invited biennially. The participation rates for each screening round are about 75%. All the screening mammograms are independently read by two radiologists. We collected information about the number of radiologist positions from all 16 breast centers in the country in 2024, while the number of screening examinations performed and the time spent on screen-reading and consensus were extracted from the screening database. We used 1 min for each screen-reading to estimate the screen-reading workload performed by the radiologists and calculated the time saved if one reader were replaced by AI.

resultsScreen-reading required a total of 6.5 man-years in BreastScreen Norway. Implementing AI as one of the two readers is thus able to reduce the screen-reading workload by 50%, from 6.5 to 3.3 man-years. The workload reduction corresponds to a reduction from 9% to 4.5% of the total workload for radiologists at the Norwegian breast centers.

conclusionImplementation of AI in mammographic screening has the potential to reduce the screen-reading workload for breast radiologists. The reduced screening volume is of moderate influence on the overall workload for breast radiologists. KEY POINTS: Question How much radiology time is expected to be saved if AI were used as one of the two readers of screening mammograms? Findings Use of AI as one of the two readers, reducing the screen reading volume by 50%, was of moderate influence on the total workload for breast radiologists. Clinical relevance Implementing AI was shown to have limited potential in saving radiologists' time in screen-reading mammograms. The main benefit of implementing AI in screen-reading might thus be related to increased sensitivity of the screening test.

Indexed as

Artificial IntelligenceBreast NeoplasmsMammographyRadiologistsWorkloadAgedEarly Detection of CancerFemaleHumansIntelligent SystemsMass ScreeningMiddle AgedNorwayTime FactorsArtificial intelligence (AI)Breast cancerEarly detection of cancerMammography

Identifiers

PMID41498825
PMCPMC13212373

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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.