Evidence map›Paper›PMID 41038938›Full record

ArticleScientific reports2025

Evaluation of prostate cancer pathology reports generated by ChatGPT to enhance patient comprehension.

Narmina Khanmammadova, Rafael Gevorkyan, Andrew S Afyouni, Andrei D Cumpanas, Daniel Jiang, Kristene Myklak, Tuan Thanh Nguyen, Sohrab N Ali, Jacob Basilius, Antônio Rebello Horta Görgen and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

15 authors.

Narmina KhanmammadovaDepartment of Urology, University of California, Irvine, CA, USA. nkhanmam@hs.uci.edu.
Rafael GevorkyanKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Andrew S AfyouniDepartment of Urology, University of California, Irvine, CA, USA.
Andrei D CumpanasDepartment of Urology, University of California, Irvine, CA, USA.
Daniel JiangDivision of Urology and Urologic Oncology, Department of Surgery, City of Hope, Duarte, CA, USA.
Kristene MyklakUrology, Golden State Urology, Fremont, CA, USA.
Tuan Thanh NguyenDepartment of Urology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh, Vietnam.
Sohrab N AliDepartment of Urology, University of California, Irvine, CA, USA.
Jacob BasiliusDepartment of Urology, University of California, Irvine, CA, USA.
Antônio Rebello Horta GörgenDepartment of Urology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
Jaime Altamirano-VillarroelDepartment of Urology, Fundación Arturo López Pérez, Santiago, Chile.
Mohammed ShahaitDepartment of Urology, University of California, Irvine, CA, USA.
Michael A DaneshvarDepartment of Urology, University of California, Irvine, CA, USA.
Thomas E AhleringDepartment of Urology, University of California, Irvine, CA, USA.
David I LeeDepartment of Urology, University of California, Irvine, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The technical language of medical reports can be difficult for laypeople to understand. Artificial intelligence (AI) tools such as OpenAI's ChatGPT present an opportunity to bridge this gap. This quality improvement initiative explored ChatGPT's potential to translate traditional prostate cancer pathology reports into patient-friendly versions. Using institutional templates, we created 25 prostate biopsy and 25 radical prostatectomy reports and converted them into patient-centered versions using ChatGPT-3.5. Nine urologists evaluated these reports. Additionally, 41 patients participating in the initiative were asked to provide feedback on their understanding of the biopsy reports. Feedback was collected using structured questionnaires and readability scores. The AI-generated biopsy and surgical pathology reports had higher mean Flesch Reading Ease Scores (49 ± 5; 50 ± 3) compared to the original reports (31 ± 15, p < 0.001; 41 ± 17, p = 0.02, respectively). Surgeons expressed positive opinions about AI-generated biopsy and surgical pathology reports, including patient-friendly language (77% and 95%, respectively). Patients also agreed that the AI-generated biopsy reports were well-structured and easy to follow (90%) and helpful in understanding their diagnosis (88%). 76% of patients preferred AI-generated reports as part of their care. These findings suggest that AI-developed reports may improve readability and support patient understanding.

Indexed as

ComprehensionProstatic NeoplasmsAgedArtificial IntelligenceBiopsyGenerative Artificial IntelligenceHumansMaleMiddle AgedProstatectomySurveys and QuestionnairesAI-generated reportsChatGPTLarge language modelsPatient-centered pathology reportsProstate cancerProstate needle biopsy

Identifiers

PMID41038938
PMCPMC12491622

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.