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Evaluation of ChatGPT-Generated Educational Patient Pamphlets for Common Interventional Radiology Procedures.
Kooraki, Soheil; Hosseiny, Melina; Jalili, Mohamamd H; Rahsepar, Amir Ali; Imanzadeh, Amir; Kim, Grace Hyun; Hassani, Cameron; Abtin, Fereidoun; Moriarty, John M; Bedayat, Arash.
Afiliação
  • Kooraki S; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA. Electronic address: Skooraki@mednet.ucla.edu.
  • Hosseiny M; Department of Radiology, University of California, San Diego (UCSD), San Diego, CA. Electronic address: Mhosseiny@ucsd.edu.
  • Jalili MH; Department of radiology and biomedical imaging, Yale New Haven Health, Bridgeport Hospital, CT. Electronic address: JaliliMHPubs@gmail.com.
  • Rahsepar AA; Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL. Electronic address: Amirali.rahsepar@gmail.com.
  • Imanzadeh A; Department of Radiology, University of California, Irvine (UCI), Irvine, CA. Electronic address: Aimanzad@hs.uci.edu.
  • Kim GH; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA. Electronic address: Gracekim@mednet.ucla.edu.
  • Hassani C; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA. Electronic address: CHassani@mednet.ucla.edu.
  • Abtin F; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA. Electronic address: FAbtin@mednet.ucla.edu.
  • Moriarty JM; Department of Radiological Sciences, Division of Interventional Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA. Electronic address: JMoriarty@mednet.ucla.edu.
  • Bedayat A; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA. Electronic address: ABedayat@mednet.ucla.edu.
Acad Radiol ; 2024 Jun 04.
Article em En | MEDLINE | ID: mdl-38839458
ABSTRACT
RATIONALE AND

OBJECTIVES:

This study aimed to evaluate the accuracy and reliability of educational patient pamphlets created by ChatGPT, a large language model, for common interventional radiology (IR) procedures. METHODS AND MATERIALS Twenty frequently performed IR procedures were selected, and five users were tasked to independently request ChatGPT to generate educational patient pamphlets for each procedure using identical commands. Subsequently, two independent radiologists assessed the content, quality, and accuracy of the pamphlets. The review focused on identifying potential errors, inaccuracies, the consistency of pamphlets.

RESULTS:

In a thorough analysis of the education pamphlets, we identified shortcomings in 30% (30/100) of pamphlets, with a total of 34 specific inaccuracies, including missing information about sedation for the procedure (10/34), inaccuracies related to specific procedural-related complications (8/34). A key-word co-occurrence network showed consistent themes within each group of pamphlets, while a line-by-line comparison at the level of users and across different procedures showed statistically significant inconsistencies (P < 0.001).

CONCLUSION:

ChatGPT-generated education pamphlets demonstrated potential clinical relevance and fairly consistent terminology; however, the pamphlets were not entirely accurate and exhibited some shortcomings and inter-user structural variabilities. To ensure patient safety, future improvements and refinements in large language models are warranted, while maintaining human supervision and expert validation.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article