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

This study explored research priorities at the intersection of artificial intelligence (AI) and antimicrobial resistance (AMR), a critical global health threat. Through expert interviews, 44 themes were identified, with key barriers being fragmented data access, integration challenges, and weak economic incentives. The top ten priorities include combination therapy, novel therapeutics, improved data acquisition, stronger public health policy, resource allocation, diagnostics, microbial evolution modelling, prediction, and surveillance. A key limitation was the lack of input from high-burden regions. To guide future work, the authors propose the BARDI framework: Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery, and Integrated Economic Prevention.