Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery

dc.contributor.authorHoward A.
dc.contributor.authorReza N.
dc.contributor.authorGreen P.L.
dc.contributor.authorYin M.
dc.contributor.authorDuffy E.
dc.contributor.authorMwandumba H.C.
dc.contributor.authorGerada A.
dc.contributor.authorHope W.
dc.contributor.correspondenceHoward A.
dc.contributor.otherMahidol University
dc.date.accessioned2025-10-16T18:18:37Z
dc.date.available2025-10-16T18:18:37Z
dc.date.issued2025-01-01
dc.description.abstractAntimicrobial resistance (AMR) is an intractable problem that has the potential to significantly limit advances in human health. Recently, the UN General Assembly (UNGA) High-Level Statement on AMR defined targets for addressing the impact of resistance on human, animal, and environmental health. For human health, the discovery and development of new antibiotics, antimicrobial stewardship programmes, antimicrobial surveillance, and infection control and prevention are all key areas. Artificial intelligence (AI) is ideally placed to help achieve the UNGA targets via its role in revealing patterns in data that are clinically indiscernible, and using that information to build clinical decision support systems. However, significant barriers remain in terms of necessary infrastructure, know-how, and the implementation of AI approaches. In this Series paper, we consider the potential applications of AI in combatting the AMR problem through drug discovery and development, antimicrobial stewardship, diagnostics, and surveillance, and their use in public health. We then discuss the technical, infrastructure, regulatory, ethical, and policy challenges that affect these domains.
dc.identifier.citationLancet Infectious Diseases (2025)
dc.identifier.doi10.1016/S1473-3099(25)00313-5
dc.identifier.eissn14744457
dc.identifier.issn14733099
dc.identifier.pmid40972630
dc.identifier.scopus2-s2.0-105018111417
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/112614
dc.rights.holderSCOPUS
dc.subjectMedicine
dc.titleArtificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery
dc.typeReview
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105018111417&origin=inward
oaire.citation.titleLancet Infectious Diseases
oairecerif.author.affiliationNational University of Singapore
oairecerif.author.affiliationUniversity of Liverpool
oairecerif.author.affiliationBoston University
oairecerif.author.affiliationNuffield Department of Medicine
oairecerif.author.affiliationLiverpool School of Tropical Medicine
oairecerif.author.affiliationKamuzu University of Health Sciences
oairecerif.author.affiliationMahidol Oxford Tropical Medicine Research Unit
oairecerif.author.affiliationUniversity Hospitals of Liverpool NHS Group

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