Evidence map›Paper›PMID 42174106›Full record

ArticleNPJ digital medicine2026

An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.

Lisa Browning, Richard T Colling, Jon Oxley, Jacqueline Birks, Nasullah Khalid Alham, Stefano Malacrino, Chuer Zhang, Abhisek Ghosh, Rosalin Cooper, Monica Dolton and 24 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

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

34 authors.

Lisa BrowningDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Richard T CollingDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Jon OxleyDepartment of Cellular Pathology, North Bristol NHS Trust, Bristol, UK.
Jacqueline BirksNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Nasullah Khalid AlhamEngineering Science, University of Oxford, Oxford, UK.
Stefano MalacrinoNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Chuer ZhangDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Abhisek GhoshDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Rosalin CooperDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Monica DoltonNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Margaret HortonPaige. AI Ltd, New York, NY, USA.
Juan A RetameroPaige. AI Ltd, New York, NY, USA.
Andrew ProtheroeDepartment of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Leila Bibi AhmedDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Nahida BanuDepartment of Cellular Pathology, North Bristol NHS Trust, Bristol, UK.
Anastasios ChatzitoliosDepartment of Cellular Pathology, North Bristol NHS Trust, Bristol, UK.
Irini DanialDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Samir Al-HyassatDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Bidisa SinhaDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Pallavi BorkarDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Pelvender GillDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Richard J BryantNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Jonathan AningBristol Urological Institute, North Bristol NHS Trust, Bristol, UK.
Kieron WhiteDepartment of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Richard SchefferNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Ewart StanislausNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
James CroftsNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
David J SneadDepartment of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Nasir RajpootDeptartment of Computer Science, University of Warwick, Warwick, UK.
Kate HuttonNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Davina HewittResearch and Development, University Hospitals Coventry and Warwickshire, Coventry, UK.
Clare DunstanNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK.
Rob ProcterDeptartment of Computer Science, University of Warwick, Warwick, UK.
Clare VerrillNuffield Departmen of Surgical Sciences, University of Oxford, Oxford, UK. Clare.Verrill@OUH.nhs.uk.

Funding

Department of Health and Social Care AI\_AWARD02269UK Research and Innovation File Ref 104689/Application Number 18181
6 · The paper itself

Abstract

Prospective evidence on clinical utility of AI in histopathology is limited. We conducted a prospective study across three National Health Service specialist centres in England to evaluate a commercially available AI system for assistance in prostate biopsy reporting. Of 1613 cases, 1049 were reported with AI-assistance. Endpoints evaluated diagnostic impact, clinical impact and workflow. Staged AI assistance (second-read) prompted case review and changed the initial diagnosis or Grade Group of 21/386(5.4%) patients, 5 of these (1.3%) potentially affecting clinical management. AI-assisted workflows showed significantly reduced mean turnaround time with concurrent-read compared to unassisted-read by 30.1 h (p < 0.0001) at one site with significant reductions in cases requiring immunohistochemistry in all sites (Odds Ratios 0.50,0.43,0.33, p < 0.0001, p = 0.01, p = 0.001). This first prospective, multi-centric evaluation demonstrates AI can enhance diagnostic accuracy, shorten turnaround times and reduce unnecessary testing. Scaled across the NHS, such improvements could improve patient care, deliver faster diagnoses and optimise laboratory efficiency, supporting adoption.

Identifiers

PMID42174106
PMCPMC13365413

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.