Recommendations for using artificial intelligence in clinical flow cytometry.
| Autor: | Ng DP; Department of Pathology, University of Utah, Salt Lake City, Utah, USA., Simonson PD; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA., Tarnok A; Department of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology, IZI, Leipzig, Germany., Lucas F; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA., Kern W; MLL Munich Leukemia Laboratory GmbH, Munich, Germany., Rolf N; BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada., Bogdanoski G; Clinical Development & Operations Quality, R&D Quality, Bristol Myers Squibb, Princeton, New Jersey, USA., Green C; Translational Science, Ozette Technologies, Seattle, Washington, USA., Brinkman RR; Dotmatics, Inc, Boston, Massachusetts, USA., Czechowska K; Metafora Biosystems, PARIS, France. |
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| Jazyk: | angličtina |
| Zdroj: | Cytometry. Part B, Clinical cytometry [Cytometry B Clin Cytom] 2024 Jul; Vol. 106 (4), pp. 228-238. Date of Electronic Publication: 2024 Feb 26. |
| DOI: | 10.1002/cyto.b.22166 |
| Abstrakt: | 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. (© 2024 International Clinical Cytometry Society.) |
| Databáze: | MEDLINE |
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