Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance Analysis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Antimicrobial resistance (AMR) is a critical global health threat and artificial intelligence (AI) presents new opportunities for our response. However, research priorities at the AI-AMR intersection remain undefined. This study aimed to identify and prioritise key areas for future investigation. Using a modified James Lind Alliance approach, we conducted semi-structured interviews with eight experts in AI and AMR between February and June 2024. Analysis of 338 coded responses revealed 44 distinct themes. Major barriers included fragmented data access, integration challenges and economic disincentives. The top ten priorities identified were: Combination Therapy, Novel Therapeutics, Data Acquisition, AMR Public Health Policy, Prioritisation, Economic Resource Allocation, Diagnostics, Modelling Microbial Evolution, AMR Prediction and Surveillance. A notable limitation was the underrepresentation of data from high-burden regions, limiting the generalisability of findings. To address these gaps, we propose the novel BARDI framework: Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery and Integrated Economic Prevention.
Date Issued
2025-09-01
Date Acceptance
2025-08-17
Citation
npj Antimicrobials and Resistance, 2025, 3, pp.1-9
ISSN
2731-8745
Publisher
Nature Research
Start Page
1
End Page
9
Journal / Book Title
npj Antimicrobials and Resistance
Volume
3
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
75
Date Publish Online
2025-09-01