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

Choi, Ji Soo; Pan, Daniel; O’Hare, Anthony; Moore, Catrin E.; Fouad, Fouad; Pareek, Manish; Purse, Bethan V.; Brass, Dom; Rawson, Timothy; Nellums, Laura B.; Lynch, Susanne; Damont, Gill; Barton, Greg; Lu, Lu; Jung, Sophie; Al-Oraibi, Amani; Pan, Daniel; Choi, Ji Soo; Choi, Mi Joo; Tarrant, Carolyn; Pareek, Manish; Stott, Katherine; Henney, Neil; Mullen, Rachel; Cutler, Suzanne; Hoyle, Andrew; Abernethy, Gavin; Powers, Simon; Mohaghegh, Mina

Choi, Ji Soo; Pan, Daniel; O’Hare, Anthony; Moore, Catrin E.; Fouad, Fouad; Pareek, Manish; Purse, Bethan V. ORCID: https://orcid.org/0000-0001-5140-2710; Brass, Dom; Rawson, Timothy; Nellums, Laura B.; Lynch, Susanne; Damont, Gill; Barton, Greg; Lu, Lu; Jung, Sophie; Al-Oraibi, Amani; Pan, Daniel; Choi, Ji Soo; Choi, Mi Joo; Tarrant, Carolyn; Pareek, Manish; Stott, Katherine; Henney, Neil; Mullen, Rachel; Cutler, Suzanne; Hoyle, Andrew; Abernethy, Gavin; Powers, Simon; Mohaghegh, Mina. 2026 Perspective: large language models and antimicrobial resistance among migrants: an equity imperative. npj Digital Medicine. 10.1038/s41746-026-02742-y

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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.

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