Digital microbiology

Egli, Adrian; Schrenzel, Jacques; Greub, Gilbert

BACKGROUND: Digitalisation and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lay ahead to digitalise the microbiological workflows. Making an efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. OBJECTIVE: This review article summarizes the most important concepts of digital microbiology. The article provides microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. SOURCES: We used peer-reviewed literature identified by a Pubmed search for digitalisation, machine learning, artificial intelligence and microbiology. CONTENT: We describe the opportunities and challenges of digitalisation in microbiological diagnostic process 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. IMPLICATIONS: We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of the 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.