Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
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)
1
Zitationen
14
Autoren
2025
Jahr
Abstract
BACKGROUND: Rare 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. METHODS: Medical 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. RESULTS: Forty-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. CONCLUSIONS: ChatGPT 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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.551 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.443 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.942 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.792 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- University Medical Center Utrecht(NL)
- Administração Regional de Saúde de Lisboa e Vale do Tejo(PT)
- Centro Hospitalar do Porto(PT)
- Gdańsk Medical University(PL)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico(IT)
- Emma Kinderziekenhuis(NL)
- Leiden University Medical Center(NL)
- Willem-Alexander Kinderziekenhuis(NL)
- Erasmus MC - Sophia Children’s Hospital(NL)
- Charité - Universitätsmedizin Berlin(DE)
- University of Cologne(DE)
- University Hospital Cologne(DE)
- Heidelberg University(DE)
- University Hospital Heidelberg(DE)