Related Experiment Video

Updated: Sep 9, 2025

Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance
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Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance

Published on: July 21, 2023

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Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance analysis

William J Waldock1, Hannah Thould1, Leonid Chindelevitch2

  • 1Institute of Global Health Innovation, Imperial College London, London, UK.

Npj Antimicrobials and Resistance
|September 1, 2025
PubMed

Insights

Artificial intelligence (AI) can combat antimicrobial resistance (AMR), but research priorities are unclear. This study identified key AI-AMR research areas, including novel therapeutics and diagnostics, to guide future efforts against this global health threat.

Area of Science:

  • Medical Informatics
  • Infectious Diseases
  • Public Health

Background:

  • Antimicrobial resistance (AMR) poses a significant global health challenge.
  • Artificial intelligence (AI) offers novel approaches to combat AMR, yet research priorities at this intersection are undefined.

Purpose of the Study:

  • To identify and prioritize key research areas at the intersection of AI and AMR.
  • To define future research directions for AI-AMR initiatives.

Main Methods:

  • A modified James Lind Alliance approach was employed.
  • Semi-structured interviews were conducted with eight experts in AI and AMR.
  • Analysis of 338 coded responses identified 44 distinct themes.

Main Results:

  • Major barriers identified include fragmented data access, integration challenges, and economic disincentives.
  • Top ten research priorities include Combination Therapy, Novel Therapeutics, Data Acquisition, AMR Public Health Policy, Prioritisation, Economic Resource Allocation, Diagnostics, Modelling Microbial Evolution, AMR Prediction, and Surveillance.
  • The BARDI framework (Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery, Integrated Economic Prevention) was proposed.

Conclusions:

  • Defining research priorities is crucial for advancing AI applications in AMR.
  • Addressing data access, economic factors, and regional representation is essential for effective AI-AMR strategies.
  • The BARDI framework offers a structured approach to guide future AI-AMR research and implementation.

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