Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.
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Acknowledgements
The authors acknowledge the National Institute of Health Research Imperial Biomedical Research Centre and the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London in partnership with Public Health England and the NIHR Imperial Patient Safety Translational Research Centre. They also acknowledge Imperial Biomedical Research Centre (BRC). R.A. is supported by an NIHR Fellowship in Knowledge Mobilisation. T.M.R., A.H.H., and P.G. are supported by funding from the National Institute for Health Research Invention for Innovation Grant (i4i), Enhanced, Personalized and Integrated Care for Infection Management at Point of Care (EPIC IMPOC), II-LA-0214-20008. This work was produced independently. The funders had no role in in study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the UK Department of Health.
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Rawson, T.M., Ahmad, R., Toumazou, C. et al. Artificial intelligence can improve decision-making in infection management. Nat Hum Behav 3, 543–545 (2019). https://doi.org/10.1038/s41562-019-0583-9
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DOI: https://doi.org/10.1038/s41562-019-0583-9
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