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Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Myths.
Huang, Sean S; Song, Qingyuan; Beiting, Kimberly J; Duggan, Maria C; Hines, Kristin; Murff, Harvey; Leung, Vania; Powers, James; Harvey, T S; Malin, Bradley; Yin, Zhijun.
Afiliación
  • Huang SS; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: sean.huang@vumc.org.
  • Song Q; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
  • Beiting KJ; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Duggan MC; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research Education and Clinical Center (GRECC), Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA.
  • Hines K; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Murff H; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Leung V; Department of Academic Internal Medicine and Geriatrics, University of Illinois at Chicago, Chicago, IL, USA.
  • Powers J; Division of Geriatrics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Geriatric Research Education and Clinical Center (GRECC), Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, USA.
  • Harvey TS; Department of Anthropology, Vanderbilt University, Nashville, TN, USA.
  • Malin B; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Yin Z; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
J Am Med Dir Assoc ; 25(10): 105178, 2024 Aug 03.
Article en En | MEDLINE | ID: mdl-39106968
ABSTRACT

INTRODUCTION:

There are many myths regarding Alzheimer's disease (AD) that have been circulated on the internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models, such as ChatGPT, may be a valuable tool to help assess these myths for veracity and inaccuracy; however, they can induce misinformation as well.

OBJECTIVE:

This study assesses ChatGPT's ability to identify and address AD myths with reliable information.

METHODS:

We conducted a cross-sectional study of attending geriatric medicine clinicians' evaluation of ChatGPT (GPT 4.0) responses to 16 selected AD myths. We prompted ChatGPT to express its opinion on each myth and implemented a survey using REDCap to determine the degree to which clinicians agreed with the accuracy of each of ChatGPT's explanations. We also collected their explanations of any disagreements with ChatGPT's responses. We used a 5-category Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' agreement in each aspect of the evaluation.

RESULTS:

The clinicians (n = 10) were generally satisfied with ChatGPT's explanations. Among the 16 myths, the clinicians were generally satisfied with these explanations, with [mean (SD) score of 1.1(±0.3)]. Most clinicians selected "Agree" or "Strongly Agree" for each statement. Some statements obtained a small number of "Disagree" responses. There were no "Strongly Disagree" responses.

CONCLUSION:

Most surveyed health care professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation; however, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Am Med Dir Assoc Asunto de la revista: HISTORIA DA MEDICINA / MEDICINA Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Am Med Dir Assoc Asunto de la revista: HISTORIA DA MEDICINA / MEDICINA Año: 2024 Tipo del documento: Article