Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study

Michael C Thrun, Jörg Hoffmann, Stefan W Krause, Peter Krawitz, Quirin Stier, Andreas Neubauer, Cornelia Brendel and Alfred Ultsch

PLOS Medicine, 2026, vol. 23, issue 7, 1-24

Abstract: Background: Multiparameter flow cytometry is a cornerstone of B cell non-Hodgkin lymphoma (B-NHL) diagnostics, but interpretation requires substantial expertise and is complicated by high-dimensional data, variable sample quality, limited data for rare entities, and evolving clinical classification systems. Current artificial intelligence approaches often require large training datasets and provide limited insight into the rationale behind individual diagnostic decisions. Methods and findings: We developed FlowXAI, a self-explaining artificial intelligence system designed to support B-NHL classification while explicitly reporting case-level diagnostic trustworthiness. FlowXAI combines unsupervised structural analysis with a clinically motivated, multi-level diagnostic framework reflecting routine diagnostic priorities. An unsupervised Tile Mining (TM) procedure performs pre-diagnostic sample-quality assessment by identifying structurally atypical samples. TM is applied to filter training data, enabling substantial reduction of training requirements while preserving unbiased evaluation on independent test samples. Conclusions: FlowXAI provides accurate, data-efficient, and transparent support for B-NHL immunophenotyping from nonstandardized flow cytometry data. By combining interpretable decision logic with explicit self-assessment, FlowXAI offers a clinically meaningful framework for diagnostic support and training, particularly in settings with limited expert availability or rare lymphoma subtypes. The main limitation is the retrospective evaluation using specific antibody panels, and FlowXAI requires prospective validation as a decision-support tool within integrated diagnostic workflows. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this diagnostic decision support study, Michael C. Thrun and colleagues develop and validate Self-explaining artificial intelligence system (FlowXAI) for the classification of B cell non-Hodgkin lymphoma.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmed00:1004889

DOI: 10.1371/journal.pmed.1004889

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