Assessing ChatGPT Responses to Alzheimer’s Disease Myths

Poster Number: P27
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Advanced Disease, Internal Medicine or Medical Subspecialty, Large Language Models (LLMs), Aging in Place
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Alzheimer's disease is prone to myths and statements that exhibit varying degrees of accuracy, inaccuracy, and misinformation. This study assesses ChatGPT's ability to identify and address AD myths with reliable information. Geriatricians acknowledged the potential value of ChatGPT in mitigating misinformation.

Speaker(s):
Sean Huang, MD
Vanderbilt University

Author(s):
Qingyuan Song, Master of Engineering - Vanderbilt University; Kimberly Beiting, MD - Vanderbilt University Medical Center; Maria Duggan, MD - Vanderbilt University Medical Center; Kristin Hines, MD - Vanderbilt University Medical Center; Harvey Murff, MD - Vanderbilt University Medical Center; Vania Leung, MD - University of Illinois at Chicago; James Powers, MD - Vanderbilt University Medical Center; T.S. Harvey, Ph.D. - Vanderbilt University; Bradley Malin, PhD - Vanderbilt University Medical Center; Zhijun Yin, Ph.D. - Vanderbilt University Medical Center;