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Using Large Language Models to Assist Antimicrobial Resistance Policy Development: Integrating the Environment into Health Protection Planning

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    Using Large Language Models to Assist Antimicrobial Resistance Policy Development: Integrating the Environment into Health Protection Planning
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    • Cai Chen
      Cai Chen
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      University of Chinese Academy of Sciences, Beijing 100049, Peoples R China
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    • Shu-Le Li
      Shu-Le Li
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      University of Chinese Academy of Sciences, Beijing 100049, Peoples R China
      More by Shu-Le Li
    • Anthony D. So
      Anthony D. So
      Innovation + Design Enabling Access (IDEA) Initiative, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
      More by Anthony D. So
    • Yao-Yang Xu
      Yao-Yang Xu
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, Peoples R China
      More by Yao-Yang Xu
      Orcidhttps://orcid.org/0000-0002-3250-8744
    • Zhao-Feng Guo
      Zhao-Feng Guo
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, Peoples R China
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    • Xinbing Wang
      Xinbing Wang
      School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
      More by Xinbing Wang
    • David W. Graham*
      David W. Graham
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      Department of Biosciences, Durham University, Durham, DH1 3LE, U.K.
      *Email: [email protected]
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    • Yong-Guan Zhu*
      Yong-Guan Zhu
      Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China
      Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, Peoples R China
      State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
      *Email: [email protected]
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      Orcidhttps://orcid.org/0000-0003-3861-8482
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2025, 59, 2, 1243–1252
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    https://doi.org/10.1021/acs.est.4c07842
    Published January 8, 2025
    Copyright © 2025 American Chemical Society
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    Abstract

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    Increasing antimicrobial resistance (AMR) poses a substantial threat to global health and economies, which has led many countries and regions to develop AMR National Action Plans (NAPs). However, inadequate logistical capacity, funding, and essential information can hinder NAP policymaking, especially in low-to-middle-income countries (LMICs). Therefore, major gaps exist between aspirations and actions, such as fully operationalized environmental AMR surveillance programs in NAPs. To help bridge knowledge gaps, we compiled a multilingual database that contains policy guidance from 146 countries composed of NAPs, internal reports, and other guidance documents on AMR mitigations, including environmental considerations. Leveraging this database, we developed an AMR-Policy GPT, a large language model with advanced retrieval-augmented generation capabilities. This prototype model can search and summarize evidence from plans, metadata, and technical knowledge and provide traceable references from global document databases. It was then manually validated to show its proficiency in accurately managing diverse inquiries while minimizing misinformation. Overall, the AMR-Policy GPT offers a prototype that, with the deepening of its database and further road testing, has the potential to support inclusive, evidence-informed AMR policy guidance to support governments, research, and public agencies. A conversational version of our prototype is available at www.liuhuibot.com/amrpolicy.

    Copyright © 2025 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.4c07842.

    • Supporting methods and results; multi-language question and answer examples; comparison of generated answers from the hybrid AMR-Policy GPT, standalone AMR-Policy GPT, and GPT-4 to ten questions (PDF)

    • Road test of AMR-Policy GPT (PDF)

    • Model performance on RAGAS framework metrics and independent evaluation and detailed information of knowledge base and included NAPs (XLSX)

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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2025, 59, 2, 1243–1252
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.est.4c07842
    Published January 8, 2025
    Copyright © 2025 American Chemical Society
    Request reuse permissions

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