Evidence map›Paper›PMID 42526933›Full record

ArticleBMJ open2026

Improving laboratory workforce efficiency using AI-assisted digital cytology within an HPV-based cervical screening programme: A model-based evaluation for the NHS Cervical Screening Programmes.

Allan Wilson, Alison Cropper, Yixuan Ma, Elisabeth J Adams, Sue Mehew, Margaret Morgan, Nichole Villeneuve, Caron Mitchell

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Allan WilsonPathology Department, Monklands Hospital, Lanarkshire, UK.
Alison CropperCytology Department, University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, Derby, UK.
Yixuan MaAquarius Population Health Limited, London, UK.
Elisabeth J AdamsAquarius Population Health Limited, London, UK elisabeth.adams@aquariusph.com.ORCID 0000-0002-4222-9394
Sue MehewPathology Department, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
Margaret MorganHealth Services Laboratories, London, UK.
Nichole VilleneuveCytology Department, North Bristol NHS Trust, Bristol, UK.
Caron MitchellCytology Department, University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, Derby, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to estimate the time required to review and report cervical cytology slides within a Human Papillomavirus (HPV)-primary cervical cancer screening programme, where cytology is used for triage following a positive HPV test, comparing Artificial Intelligence (AI)-assisted digital cytology (Genius) with manual microscopy.

designA decision-tree model of the flow of slides through the National Health Service (NHS) England cervical screening cytology laboratory workflow, parameterised using published evidence and expert input.

settingCytology laboratories within the NHS Cervical Screening Programme in England and Scotland.

resultsScreening and reporting 479,125 cytology slides annually in England was estimated to require 31,842 staff hours with AI-assisted digital cytology versus 103,151 hours with manual microscopy. The mean time per slide review and reporting was 4.0 vs 12.9 minutes, respectively, corresponding to a potential 69% increase in overall productivity.

conclusionsAI-assisted digital cytology can support more resilient cervical screening programmes in the UK and other countries by reducing staff time required for slide review and reporting. These findings may inform policymakers considering strategies to mitigate workforce shortages.

Indexed as

Artificial IntelligenceEarly Detection of CancerVaginal SmearsDecision TreesFemaleHumansNational Health ProgramsUnited KingdomWorkflowCYTOPATHOLOGYDigital TechnologyHealth WorkforceUterine Cervical Neoplasms

Identifiers

PMID42526933
PMCPMC13422948

What Socratic holds

Textmetadata
LicenceCC BY-NC
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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 Socratic graph.