Recommendations for using artificial intelligence in clinical flow cytometry.

TitleRecommendations for using artificial intelligence in clinical flow cytometry.
Publication TypeJournal Article
Year of Publication2024
AuthorsNg DP, Simonson PD, Tarnok A, Lucas F, Kern W, Rolf N, Bogdanoski G, Green C, Brinkman RR, Czechowska K
JournalCytometry B Clin Cytom
Date Published2024 Feb 26

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Alternate JournalCytometry B Clin Cytom
PubMed ID38407537
Related Faculty: 
Paul Simonson, M.D., Ph.D.

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