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Publications

Journal Article Review Ahead of print

PubMed: 42135455 DOI: 10.1038/s41746-026-02742-y

Perspective: large language models and antimicrobial resistance among migrants: an equity imperative.

NPJ Digit Med

Authors: Ji Soo Choi, Daniel Pan, Anthony O'Hare, Catrin E Moore, Fouad Fouad, Manish Pareek

Affiliations

  • Development Centre for Population Health, University of Leicester, Leicester, UK. Department of Respiratory Sciences, University of Leicester, Leicester, UK. Leicester NIHR Biomedical Research Centre, Leicester, UK. Department of Infectious Diseases and HIV Medicine, Leicester, UK.
  • Development Centre for Population Health, University of Leicester, Leicester, UK.
  • Department of Respiratory Sciences, University of Leicester, Leicester, UK.
  • Leicester NIHR Biomedical Research Centre, Leicester, UK.
  • Department of Infectious Diseases and HIV Medicine, Leicester, UK.
  • Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, UK.
  • Centre for Neonatal and Paediatric Infection, Institute for Infection and Immunity, City St George's, University of London, London, UK.
  • Liverpool School of Tropical Medicine, Liverpool, UK.
  • Development Centre for Population Health, University of Leicester, Leicester, UK.
  • Department of Respiratory Sciences, University of Leicester, Leicester, UK.
  • Leicester NIHR Biomedical Research Centre, Leicester, UK.
  • Department of Infectious Diseases and HIV Medicine, Leicester, UK.

Abstract

Despite progress in antimicrobial resistance (AMR) surveillance, migrants and ethnic minorities, who bear disproportionate AMR burdens, remain underrepresented in programmes. Digital health is common, but we found no interventions using large language models (LLMs) to reduce AMR in these communities. In three workshops, we identified priorities: culturally and linguistically inclusive design; context specific knowledge from community settings; and trust building via community health workers, with data protection and bias mitigation.

MeSH

Language