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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics

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

Cai Chen1,2, Shu-Le Li1,2, Anthony D So3

  • 1Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, Peoples R China.

Environmental Science & Technology
|January 8, 2025
PubMed
Summary

Antimicrobial resistance (AMR) action plans face challenges, especially in LMICs. A new AI tool, AMR-Policy GPT, uses a global policy database to support evidence-based AMR mitigation strategies.

Keywords:
LMICsantimicrobial resistanceartificial intelligencelarge language modelone healthpolicymakingretrieval-augmented generation

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Area of Science:

  • Global Health Security
  • Environmental Science
  • Public Policy

Background:

  • Rising antimicrobial resistance (AMR) threatens global health and economies, prompting national action plans (NAPs).
  • Low-to-middle-income countries (LMICs) face significant barriers in NAP implementation due to limited resources and data.
  • Gaps persist between AMR policy goals and practical execution, particularly in environmental surveillance.

Purpose of the Study:

  • To address knowledge gaps in AMR policy development and implementation.
  • To create a comprehensive, multilingual database of global AMR policy documents.
  • To develop and validate an AI-powered tool to support evidence-informed AMR policymaking.

Main Methods:

  • Compiled a multilingual database of AMR policy guidance from 146 countries, including NAPs and related documents.
  • Developed the AMR-Policy GPT, a large language model utilizing retrieval-augmented generation.
  • Validated the AI model's accuracy in searching, summarizing, and referencing information from diverse policy documents.

Main Results:

  • A comprehensive database of AMR policy documents was successfully created.
  • The AMR-Policy GPT demonstrated proficiency in retrieving, summarizing, and citing evidence from policy documents.
  • Manual validation confirmed the AI's accuracy and ability to minimize misinformation in response to inquiries.

Conclusions:

  • The AMR-Policy GPT serves as a prototype to bridge knowledge and logistical gaps in AMR policy.
  • The tool has the potential to support governments and agencies in developing inclusive, evidence-based AMR strategies.
  • Further database expansion and testing are recommended to enhance the tool's capabilities for global AMR mitigation.