Evidence map›Paper›PMID 41221432›Full record

ArticleBJUI compass2025

Exploring patient and clinician opinions, perspectives and acceptance of the use of artificial intelligence in the histological diagnosis of prostate cancer.

Lisa Browning, Abhisek Ghosh, Monica Dolton, Kate Hutton, Jacqueline Birks, Richard Scheffer, Ewart Stanislaus, James Crofts, Richard Colling, Richard Bryant and 1 more

Abstract read
In one paragraph

Article in BJUI compass, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Lisa BrowningDepartment of Cellular Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK.ORCID https://orcid.org/0000-0003-4254-333X
Abhisek GhoshDepartment of Cellular Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK.ORCID https://orcid.org/0000-0001-6719-6632
Monica DoltonNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Kate HuttonNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Jacqueline BirksNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Richard SchefferNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Ewart StanislausNuffield Department of Surgical Sciences University of Oxford Oxford UK.
James CroftsNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Richard CollingDepartment of Cellular Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK.
Richard BryantNuffield Department of Surgical Sciences University of Oxford Oxford UK.
Clare VerrillDepartment of Cellular Pathology Oxford University Hospitals NHS Foundation Trust Oxford UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to explore the opinions and attitudes of patients and clinicians regarding the use of artificial intelligence (AI) in the diagnosis of prostate biopsies, with a focus on acceptance and trust in the use of AI, and factors that may impact this. Subjects and methods: Surveys were sent to patient members of UK-based prostate cancer support groups and to a group of clinicians managing patients with prostate cancer (or suspected prostate cancer). Results: Of 130 patient respondents, 94% expressed acceptance of AI assistance in the diagnosis of prostate biopsies when a pathologist retains responsibility for the final diagnosis, but regard it as the responsibility of the pathologist to decide whether AI is used in this setting. Similar responses were noted among the nine clinician respondents. Regarding factors with potential impact on acceptance of AI, an understanding of how the AI was tested and its performance in comparison with a pathologist was considered to be more important than how the technology was developed, and half (51%) of patients suggested that access to additional information might impact their acceptance of AI. Conclusion: Understanding the perspectives of stakeholders is key to the successful clinical implementation of AI in the histological diagnosis of prostate biopsies. Our study shows a high level of acceptance of AI for the diagnosis of prostate biopsies among patients if a pathologist retains oversight of the diagnosis and the decision as to when AI is used. Furthermore, it suggests similar levels of acceptance among clinicians. Our study provides insight into areas for educational focus to enhance understanding of AI in this setting.

Indexed as

artificial intelligencecliniciandiagnosishistopathologypatientprostate cancertrusturology

Identifiers

PMID41221432
PMCPMC12598096

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.