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Artificial Intelligence in Infectious Diseases

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Artificial Intelligence in Medicine

Abstract

The management of infectious diseases lends itself to the application of artificial intelligence. The treatment of infection is complex, requiring the consideration of a large number of dynamic variables to inform decision-making. This includes considering organism, host, and drug factors in the context of local disease epidemiology and potential long-term consequences of anti-infective use, such as the development of antimicrobial resistance. The heterogeneity of clinical presentation caused by the same pathogen means that in many cases there is a paucity of data available to guide decision-making in real time, with individualized decisions made based on the individual patient and available data. Within this chapter we explore current applications of artificial intelligence in (i) the laboratory detection of microorganisms, (ii) the clinical diagnosis and management of infectious diseases, and (iii) the surveillance of infection. This chapter will not address other potential areas for the application of AI in infectious diseases that include anti-infective drug development, targeting infection prevention activity, and public health decision-making. We highlight potential future directions for AI in infectious diseases within the areas explored by this chapter and current barriers to wider adoption of such systems.

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Acknowledgments

The authors would like to acknowledge Dr. Pantelis Georgiou, Dr. Pau Herrero, and Dr. Bernard Hernandez from the Center for Bioinspired technology, Imperial College London, UK. They also acknowledge 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 Center. The Department of Health and Social Care funded Center for Antimicrobial Optimization (CAMO), Imperial College London, provides state-of-the-art research facilities and consolidates multidisciplinary academic excellence, clinical expertise, Imperial’s NIHR/Wellcome funded Clinical Research Facility (CRF), and partnerships with the NHS to support and deliver innovative research on antimicrobial optimization and precision prescribing. 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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Authors and Affiliations

  1. Centre for Antimicrobial Optimisation, Imperial College London, London, UK

    Timothy Miles Rawson, Nathan Peiffer-Smadja & Alison Holmes

  2. Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Hospital, London, UK

    Timothy Miles Rawson, Nathan Peiffer-Smadja & Alison Holmes

  3. Université de Paris, Inserm, IAME, Paris, France

    Nathan Peiffer-Smadja

Authors
  1. Timothy Miles Rawson
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  2. Nathan Peiffer-Smadja
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  3. Alison Holmes
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Correspondence to Alison Holmes .

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Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Rawson, T.M., Peiffer-Smadja, N., Holmes, A. (2021). Artificial Intelligence in Infectious Diseases. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_103-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_103-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58080-3

  • Online ISBN: 978-3-030-58080-3

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