Evidence map›Paper›PMID 40237816›Full record

ArticlePediatric nephrology (Berlin, Germany)2025

Risks and benefits of ChatGPT in informing patients and families with rare kidney diseases: an explorative assessment by the European Rare Kidney Disease Reference Network (ERKNet).

Albertien M van Eerde, Ana Teixeira, Flavia Galletti, Michal Maternik, Valentina Capone, Rik Westland, Jaap Mulder, Jan Halbritter, Thomas Osterholt, Valentina Neukel and 4 more

Abstract read
In one paragraph

Article in Pediatric nephrology (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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
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4 · The record

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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

14 authors.

Albertien M van EerdeDepartment of Genetics, University Medical Center, Utrecht, The Netherlands.
Ana TeixeiraDivision of Pediatric Nephrology, Centro Materno-Infantil Do Norte, Unidade Local de Saúde de Santo António, Porto, Portugal.
Flavia GallettiPKD International, Geneva, Switzerland.
Michal MaternikDepartment of Pediatrics, Nephrology, Hypertension, Medical University of Gdansk, Gdansk, Poland.
Valentina CaponePediatric Nephrology, Dialysis and Transplant Unit, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy.
Rik WestlandDepartment of Pediatric Nephrology, Emma Children'S Hospital - Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.
Jaap MulderDepartment of Pediatrics, Division of Pediatric Nephrology, Sophia Children's Hospital, Erasmus MC, Rotterdam, The Netherlands.
Jan HalbritterDepartment of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, Berlin, Germany.
Thomas OsterholtDepartment II of Internal Medicine and Center for Molecular Medicine Cologne, Faculty of Medicine, University Hospital of Cologne, University of Cologne, Cologne, Germany.
Valentina NeukelDivision of Pediatric Nephrology, Center for Pediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany.
Lutz T WeberDepartment of Pediatrics, Faculty of Medicine, University Hospital Cologne and University of Cologne, Cologne, Germany.
Max C LiebauDepartment of Pediatrics, Faculty of Medicine, University Hospital Cologne and University of Cologne, Cologne, Germany.
Franz SchaeferDivision of Pediatric Nephrology, Center for Pediatrics and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany.
Stefan KohlDepartment of Pediatrics, Faculty of Medicine, University Hospital Cologne and University of Cologne, Cologne, Germany. stefan.kohl@uk-koeln.de.ORCID 0000-0001-9600-8231

Funding

Projekt DEAL
6 · The paper itself

Abstract

backgroundRare diseases affect fewer than 1 in 2000 individuals, but approximately 150 rare kidney diseases account for about 10% of the chronic kidney disease (CKD) population, impacting millions across Europe and globally. The scarcity of medical experts for these conditions results in an unmet need for accurate and helpful patient information. Large language models like ChatGPT may offer a technological solution to assist medical professionals in educating patients and improving doctor-patient communication. We hypothesized that ChatGPT could provide accurate responses to frequently asked basic questions from patients with rare kidney diseases.

methodsMedical professionals and members of European Patient Advocacy Groups (ePAGs) affiliated with the European Rare Kidney Disease Reference Network (ERKNet) simulated patient-ChatGPT interactions using a Microsoft forms questionnaire and ChatGPT 3.5 and 4.0. Participants selected any rare kidney disease for a structured conversation with ChatGPT 3.5 or 4.0. Responses were evaluated for accuracy and helpfulness.

resultsForty-six ERKNet experts and 12 ePAGs from 13 European countries participated in this study. ChatGPT provided scientifically accurate and helpful information on 28 randomly selected rare kidney diseases, including prognostic information and genetic testing guidance. Participants expressed neutral positions regarding ChatGPT's recommendations on alternative treatments, second opinions, and other information sources. While ChatGPT generally was perceived as helpful and empathetic, concerns about patient safety persisted.

conclusionsChatGPT exhibited substantial potential in addressing patient inquiries regarding rare kidney diseases in a real-world context. While it demonstrated resilience against misinformation in this application, careful human oversight remains essential and indispensable.

Indexed as

Kidney DiseasesPatient Education as TopicPhysician-Patient RelationsRare DiseasesCommunicationEuropeGenerative Artificial IntelligenceHumansRisk AssessmentSurveys and QuestionnairesAIArtificial intelligenceChatGPTGenetic testingLarge language modelPatient advocacyPatient educationRare diseaseRare kidney disease

Identifiers

PMID40237816
PMCPMC12296756

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LicenceCC BY
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Registered trials

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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.