AD-AutoGPT: An autonomous GPT for Alzheimer’s disease infodemiology
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
- Xiaobai Yao (ORCID: https://orcid.org/0000-0003-2719-2017)
- Suhang Song (ORCID: https://orcid.org/0000-0003-1934-632X)
- Tai‐Quan Peng (ORCID: https://orcid.org/0000-0002-2588-7491)
- Donglan Zhang (ORCID: https://orcid.org/0000-0001-5225-4721)
- Gengchen Mai (ORCID: https://orcid.org/0000-0002-7818-7309)
- Dajiang Zhu (ORCID: https://orcid.org/0000-0002-6940-3911)
- Haixing Dai (ORCID: https://orcid.org/0000-0003-0409-6129)
- Quanzheng Li (ORCID: https://orcid.org/0000-0002-9651-5820)
- Zihao Wu (ORCID: https://orcid.org/0000-0001-7483-6570)
- Lu Shi (ORCID: https://orcid.org/0000-0002-8973-3600)
- Xiang Li (ORCID: https://orcid.org/0000-0002-9851-6376)
- Zhuo Chen (ORCID: https://orcid.org/0000-0002-5351-3489)
- Lin Zhao (ORCID: https://orcid.org/0000-0003-2567-627X)
- Yiwei Li (ORCID: https://orcid.org/0000-0002-8380-4164)
- Tianming Liu (ORCID: https://orcid.org/0000-0002-8132-9048)
- Li Sheng (ORCID: https://orcid.org/0000-0002-6649-2840)
- Shen Ye (ORCID: https://orcid.org/0000-0002-0090-7855)
- Zhengliang Liu (ORCID: https://orcid.org/0000-0001-7061-6714)
Institutions
- Harvard University (US)
- Long Island University (US)
- University of Georgia (US)
- The University of Texas at Arlington (US)
- Massachusetts General Hospital (US)
- Michigan United (US)
- University of Virginia (US)
- Clemson University (US)
- The University of Texas at Austin (US)
- Michigan State University (US)
Publication Details
- Journal
- PLOS Global Public Health
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1371/journal.pgph.0004383
- Citations
- 2
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 4.88
Funders
- University of Georgia
- National Institutes of Health