Evidence map›Paper›PMID 39143247›Full record

ArticleEuropean radiology2025

Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

Stefan J Fransen, T C Kwee, D Rouw, C Roest, Q Y van Lohuizen, F F J Simonis, P J van Leeuwen, S Heijmink, Y P Ongena, M Haan and 1 more

Abstract readMulticenter Study
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. The use of generative AI tools among individuals with cancer and their caregivers: a cross-sectional study of awareness, attitudes, and experiences.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Stefan J FransenUniversity Medical Center Groningen, Groningen, Netherlands. s.j.fransen@umcg.nl.ORCID http://orcid.org/0000-0002-5454-5052
T C KweeUniversity Medical Center Groningen, Groningen, Netherlands.
D RouwMartini Hospital, Groningen, Netherlands.
C RoestUniversity Medical Center Groningen, Groningen, Netherlands.
Q Y van LohuizenUniversity Medical Center Groningen, Groningen, Netherlands.
F F J SimonisTwente Technical University, Enschede, Netherlands.
P J van LeeuwenDutch Cancer Institute, Amsterdam, Netherlands.
S HeijminkDutch Cancer Institute, Amsterdam, Netherlands.
Y P OngenaUniversity of Groningen, Groningen, Netherlands.
M HaanUniversity of Groningen, Groningen, Netherlands.
D YakarUniversity Medical Center Groningen, Groningen, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. MATERIALS AND

methodsA prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis.

resultsA total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis.

conclusionsPatients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. CLINICAL RELEVANCE STATEMENT: Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. KEY POINTS: Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.

Indexed as

Artificial IntelligenceMagnetic Resonance ImagingProstatic NeoplasmsAgedHumansMaleMiddle AgedProspective StudiesSurveys and QuestionnairesArtificial intelligenceMagnetic resonance imagingPatient preferenceProstate cancerQuestionnaire

Identifiers

PMID39143247
PMCPMC11782406

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.