Laboratory automation with Artificial Intelligence (AI) features have now emerged into routine diagnostic clinical use to interpret growth on agar plates. Applications are currently available to assess urine cultures and infection control screens, yet some of the details around the development of AI algorithms remain entrenched with AI development specialists and are not well understood by laboratorians. The generation of algorithms is not a trivial task and is a highly structured process, with several considerations needed to develop the appropriate data for specific intended uses. Understanding these considerations highlights the limitations of any algorithm created and informs better design practices so that algorithm objectives can be thoroughly tested prior to routine use.

This perspective and thought-leadership commentary from microbiology AI-development specialists gives insight into the detailed development processes that underpin the suites of APAS Analysis Modules. This is the first such peer-reviewed journal to do so and will be a key reference point as this field advances.

Journal: Frontiers in Microbiology

‍Date: August 2022

‍Authors: DeYoung, B., Morales, M. and Giglio, S.

‍Citation: DeYoung, B., Morales, M. and Giglio, S., Microbiology 2.0 - A 'behind the scenes' consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories. Frontiers in Microbiology, p.2898.