Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance.

Howard, Alex ORCID: 0000-0002-4195-6821, Aston, Stephen, Gerada, Alessandro ORCID: 0000-0002-6743-4271, Reza, Nada, Bincalar, Jason ORCID: 0000-0002-0587-6437, Mwandumba, Henry, Butterworth, Tom, Hope, William ORCID: 0000-0001-6187-878X and Buchan, Iain (2024) Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance. The Lancet. Digital health, 6 (1). e79-e86.

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Official URL: http://dx.doi.org/10.1016/s2589-7500(23)00221-2

Abstract

The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.

Item Type:	Article
Uncontrolled Keywords:	Humans, Anti-Infective Agents, Anti-Bacterial Agents, Drug Resistance, Bacterial, Artificial Intelligence, Health Facilities
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	25 Jan 2024 09:02
Last Modified:	25 Jan 2024 09:03
DOI:	10.1016/s2589-7500(23)00221-2
Open Access URL:	https://doi.org/10.1016/S2589-7500(23)00221-2
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177999