Evidence map›Paper›PMID 40465696›Full record

Trial reportPloS one2025

GPT-4 generates accurate and readable patient education materials aligned with current oncological guidelines: A randomized assessment.

Severin Rodler, Francesco Cei, Conner Ganjavi, Enrico Checcucci, Pieter De Backer, Ines Rivero Belenchon, Mark Taratkin, Stefano Puliatti, Alessandro Veccia, Pietro Piazza and 13 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Methodological Considerations in Audiovisual Cancer Education Framework for Low-Resource Settings: An Analysis of the Emad et al. Approach.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
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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

23 authors.

Severin RodlerUSC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.
Francesco CeiUSC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.
Conner GanjaviUSC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.
Enrico CheccucciDepartment of Surgery, Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy.
Pieter De BackerDepartment of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium.
Ines Rivero BelenchonUrology and Nephrology Department, Virgen del Rocío University Hospital, Seville, Spain.
Mark TaratkinInstitute for Urology and Reproductive Health, Sechenov University, Moscow, Russia.ORCID https://orcid.org/0000-0003-4369-173X
Stefano PuliattiDepartment of Urology, University of Modena and Reggio Emilia, Modena, Italy.ORCID https://orcid.org/0000-0002-1597-9595
Alessandro VecciaUrology Unit, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy.
Pietro PiazzaDivision of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Loïc BaekelandtDepartment of Urology, University Hospitals Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0002-8962-9395
Karl-Friedrich KowalewskiDepartment of Urology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.
Juan Gómez RivasDepartment of Urology, Hospital Clinico San Carlos, Madrid, Spain.
Christian D FankhauserDepartment of Urology, University of Zurich, Zurich, Switzerland.
Marco MoschiniDepartment of Urology, IRCCS San Raffaele Hospital and Vita-Salute San Raffaele University, Milan, Italy.
Giorgio GandagliaDepartment of Urology, IRCCS San Raffaele Hospital and Vita-Salute San Raffaele University, Milan, Italy.
Riccardo CampiUnit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy.ORCID https://orcid.org/0000-0001-5237-0888
Andre De Castro AbreuUSC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.
Giorgio I RussoUrology Section, University of Catania, Catania, Italy.
Andrea CocciDepartment of Urology, University of Florence, Careggi Hospital, Florence, Italy.
Serena MarucciaIstituti Clinici Zucchi - Gruppo San Donato, MB, Italy.
Giovanni E CacciamaniUSC Institute of Urology and Catherine and Joseph Aresty Department of Urology, University of Southern California, Los Angeles, California, United States of America.ORCID https://orcid.org/0000-0002-8892-5539
YAU Collaborators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

aimGuideline-based patient educational materials (PEMs) empower patients and reduce misinformation, but require frequent updates and must be adapted to the readability level of patients. The aim is to assess whether generative artificial intelligence (GenAI) can provide readable, accurate, and up-to-date PEMs that can be subsequently translated into multiple languages for broad dissemination. STUDY DESIGN AND

methodsThe European Association of Urology (EAU) guidelines for prostate, bladder, kidney, and testicular cancer were used as the knowledge base for GPT-4 to generate PEMs. Additionally, the PEMs were translated into five commonly spoken languages within the European Union (EU). The study was conducted through a single-blinded, online randomized assessment survey. After an initial pilot assessment of the GenAI-generated PEMs, thirty-two members of the Young Academic Urologists (YAU) groups evaluated the accuracy, completeness, and clarity of the original versus GPT-generated PEMs. The translation assessment involved two native speakers from different YAU groups for each language: Dutch, French, German, Italian, and Spanish. The primary outcomes were readability, accuracy, completeness, faithfulness, and clarity. Readability was measured using Flesch Kincaid Reading Ease (FKRE), Flesch Kincaid Grade Level (FKGL), Gunning Fog (GFS) scores and Smog (SI), Coleman Liau (CLI), Automated Readability (ARI) indexes. Accuracy, completeness, faithfulness, and clarity were rated on a 5-item Likert scale.

resultsThe mean time to create layperson PEMs based on the latest guideline by GPT-4 was 52.1 seconds. The readability scores for the 8 original PEMs were lower than for the 8 GPT-4-generated PEMs (Mean FKRE: 43.5 vs. 70.8; p < .001). The required reading education levels were higher for original PEMs compared to GPT-4 generated PEMs (Mean FKGL: 11.6 vs. 6.1; p < .001). For all urological localized cancers, the original PEMs were not significantly different from the GPT-4 generated PEMs in accuracy, completeness, and clarity. Similarly, no differences were observed for metastatic cancers. Translations of GPT-generated PEMs were rated as faithful in 77.5% of cases and clear in 67.5% of cases. CONCLUSIONS AND RELEVANCE: GPT-4 generated PEMs have better readability levels compared to original PEMs while maintaining similar accuracy, completeness, and clarity. The use of GenAI's information extraction and language capabilities, integrated with human oversight, can significantly reduce the workload and ensure up-to-date and accurate PEMs. PATIENT SUMMARY: Some cancer facts made for patients can be hard to read or not in the right words for those with prostate, bladder, kidney, or testicular cancer. This study used AI to quickly make short and easy-to-read content from trusted facts. Doctors checked the AI content and found that they were just as accurate, complete, and clear as the original text made for patients. They also worked well in many languages. This AI tool can assist providers in making it easier for patients to understand their cancer and the best care they can get.

Indexed as

Patient Education as TopicPractice Guidelines as TopicArtificial IntelligenceComprehensionFemaleHumansMale

Identifiers

PMID40465696
PMCPMC12136319

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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.