AD-AutoGPT: An autonomous GPT for Alzheimer's disease infodemiology.

Journal: PLOS global public health
Published Date: May 7, 2025

Abstract

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT, which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance map visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes. Code, a demo video, and other information are available at https://github.com/levyisthebest/AD-AutoGPT.

Authors

  • Haixing Dai
    School of Computing, University of Georgia, Athens, GA, United States.
  • Yiwei Li
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Zhengliang Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Lin Zhao
    c Key Laboratory of Birth Defects and Related Diseases of Women and Children (Ministry of Education) , West China Second University Hospital Sichuan University , Chengdu , China.
  • Zihao Wu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Suhang Song
    College of Public Health, University of Georgia, Athens, Georgia, United States of America.
  • Shen Ye
    Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America.
  • Dajiang Zhu
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Sheng Li
    School of Data Science, University of Virginia, Charlottesville, VA, United States.
  • Xiaobai Yao
    Department of Geography, University of Georgia, Athens, Georgia, United States of America.
  • Lu Shi
    Department of Pharmacy, Jianghan University, Wuhan, 430056, China.
  • Tai-Quan Peng
    Department of Communication, Michigan State University, East Lansing, Michigan, United States of America.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Zhuo Chen
    State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China. Electronic address: gychenzhuo@aliyun.com.
  • Donglan Zhang
    NYU Long Island School of Medicine, New York University, Mineola, New York, United States of America.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Gengchen Mai
    Department of Geography, University of Georgia, Athens, Georgia, United States of America.

Keywords

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