• A Egli, J Schrenzel, and G Greub.
• Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland; Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland. Electronic address: adrian.egli@usb.ch.
• Clin. Microbiol. Infect. 2020 Oct 1; 26 (10): 1324-1331.

BackgroundDigitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.ObjectiveThis review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.SourcesWe used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.ContentWe describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.ImplicationsWe predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

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