{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:35:58Z","timestamp":1793147758259,"version":"4.5.0"},"update-to":[{"DOI":"10.1371\/journal.pmed.1004889","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000}}],"reference-count":78,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosmedicine.org"],"crossmark-restriction":false},"short-container-title":["PLoS Med"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                    <jats:p>FlowXAI was evaluated using repeated cross-validation on 19,493 peripheral blood samples and further assessed on an independent external benchmark dataset generated at a separate diagnostic center using a different antibody panel. Across diagnostic levels, FlowXAI achieved performance comparable to a deep learning\u2013based system despite requiring approximately two orders of magnitude fewer training samples. When predictions were classified as confident by the system\u2019s internal self-assessment, diagnostic performance exceeded that of the neural network baseline. Unsupervised structural analysis demonstrated clear separation between normal controls and selected lymphoma entities such as chronic lymphocytic leukemia\u2013like lymphomas and hairy cell leukemia, while other entities were not clearly separable using the antibody panels studied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pmed.1004889","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T17:43:01Z","timestamp":1783964581000},"page":"e1004889","update-policy":"https:\/\/doi.org\/10.1371\/journal.pmed.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study"],"prefix":"10.1371","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-5543","authenticated-orcid":true,"given":"Michael C.","family":"Thrun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rg","family":"Hoffmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5259-4651","authenticated-orcid":true,"given":"Stefan W.","family":"Krause","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-8625","authenticated-orcid":true,"given":"Peter","family":"Krawitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quirin","family":"Stier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Neubauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-9557","authenticated-orcid":true,"given":"Cornelia","family":"Brendel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred","family":"Ultsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"issue":"26","key":"pmed.1004889.ref001","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.1408792111","article-title":"Automated identification of stratifying signatures in cellular subpopulations","volume":"111","author":"RV Bruggner","year":"2014","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"9","key":"pmed.1004889.ref002","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1093\/bioinformatics\/btt770","article-title":"Enhanced flowType\/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data","volume":"30","author":"K O\u2019Neill","year":"2014","journal-title":"Bioinformatics"},{"issue":"9","key":"pmed.1004889.ref003","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1109\/JBHI.2022.3175514","article-title":"A chunking-for-pooling strategy for cytometric representation learning for automatic hematologic malignancy classification","volume":"26","author":"J-L Li","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"pmed.1004889.ref004","doi-asserted-by":"crossref","unstructured":"Kowarsch F, Weijler L, W\u00f6dlinger M, Reiter M, Maurer-Granofszky M, Schumich A. Towards self-explainable transformers for cell classification in flow cytometry data. 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- Towards a typology of the concept of explanation for the design of explainable AI","volume":"213","author":"F Cabitza","year":"2023","journal-title":"Expert Syst Appl"},{"issue":"2","key":"pmed.1004889.ref017","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/info13020051","article-title":"Exploiting distance-based structures in data using an explainable AI for stock picking","volume":"13","author":"MC Thrun","year":"2022","journal-title":"Information"},{"issue":"1","key":"pmed.1004889.ref018","doi-asserted-by":"crossref","first-page":"170","DOI":"10.3390\/make3010009","article-title":"Explainable AI framework for multivariate hydrochemical time series","volume":"3","author":"MC Thrun","year":"2021","journal-title":"MAKE"},{"key":"pmed.1004889.ref019","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s13218-022-00782-6","article-title":"Identification of explainable structures in data with a human-in-the-loop","volume":"36","author":"MC Thrun","year":"2022","journal-title":"KI Kunstl Intell"},{"key":"pmed.1004889.ref020","doi-asserted-by":"crossref","unstructured":"Goebel R, Chander A, Holzinger K, Lecue F, Akata Z, Stumpf S. Explainable AI: the new 42?. In: Machine Learning and Knowledge Extraction: Second IFIP TC 5, TC 8\/WG 84, 89, TC 12\/WG 129 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27\u201330, 2018, Proceedings, 2018. pp. 295\u2013303. https:\/\/doi.org\/10.1007\/978-3-319-99740-7_21","DOI":"10.1007\/978-3-319-99740-7_21"},{"issue":"1","key":"pmed.1004889.ref021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/biomedinformatics2010001","article-title":"Explainable Artificial Intelligence (XAI) in biomedicine: making AI decisions trustworthy for physicians and patients","volume":"2","author":"J L\u00f6tsch","year":"2021","journal-title":"BioMedInformatics"},{"key":"pmed.1004889.ref022","first-page":"427","article-title":"The Next Frontier: AI We Can Really Trust","author":"A Holzinger","year":"2021","journal-title":"Communications in computer and information science. Springer International Publishing"},{"issue":"11","key":"pmed.1004889.ref023","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1145\/3458652","article-title":"Medical artificial intelligence: the European legal perspective","volume":"64","author":"K St\u00f6ger","year":"2021","journal-title":"Commun ACM"},{"key":"pmed.1004889.ref024","article-title":"What do we need to build explainable AI systems for the medical domain?","author":"A Holzinger","year":"2017","journal-title":"arXiv preprint"},{"issue":"3","key":"pmed.1004889.ref025","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1002\/bjs.9736","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement","volume":"102","author":"GS Collins","year":"2015","journal-title":"Br J Surg"},{"key":"pmed.1004889.ref026","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"GS Collins","year":"2024","journal-title":"BMJ"},{"issue":"10","key":"pmed.1004889.ref027","first-page":"1640","article-title":"The immunological profile of B-cell disorders and proposal of a scoring system for the diagnosis of CLL","volume":"8","author":"E Matutes","year":"1994","journal-title":"Leukemia"},{"issue":"4","key":"pmed.1004889.ref028","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1093\/ajcp\/108.4.378","article-title":"Improvement of the chronic lymphocytic leukemia scoring system with the monoclonal antibody SN8 (CD79b)","volume":"108","author":"EJ Moreau","year":"1997","journal-title":"Am J Clin Pathol"},{"key":"pmed.1004889.ref029","doi-asserted-by":"crossref","first-page":"108382","DOI":"10.1016\/j.dib.2022.108382","article-title":"Flow cytometry datasets consisting of peripheral blood and bone marrow samples for the evaluation of explainable artificial intelligence methods","volume":"43","author":"MC Thrun","year":"2022","journal-title":"Data Brief"},{"issue":"1","key":"pmed.1004889.ref030","doi-asserted-by":"crossref","first-page":"197","DOI":"10.3390\/biomedinformatics4010013","article-title":"An Explainable AI system for the diagnosis of high-dimensional biomedical data","volume":"4","author":"A Ultsch","year":"2024","journal-title":"BioMedInformatics"},{"issue":"1","key":"pmed.1004889.ref031","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.52879","article-title":"Concordance of diagnosis of autism spectrum disorder made by pediatricians vs a multidisciplinary specialist team","volume":"6","author":"M Penner","year":"2023","journal-title":"JAMA Netw Open"},{"key":"pmed.1004889.ref032","volume-title":"Pattern recognition","author":"CM Bishop","year":"2006"},{"key":"pmed.1004889.ref033","volume-title":"Deep learning","author":"I Goodfellow","year":"2016"},{"issue":"6","key":"pmed.1004889.ref034","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0177678","article-title":"Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric","volume":"12","author":"S Boughorbel","year":"2017","journal-title":"PLoS One"},{"issue":"1","key":"pmed.1004889.ref035","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"D Chicco","year":"2020","journal-title":"BMC Genomics"},{"issue":"10","key":"pmed.1004889.ref036","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0238835","article-title":"Analyzing the fine structure of distributions","volume":"15","author":"MC Thrun","year":"2020","journal-title":"PLoS One"},{"issue":"49","key":"pmed.1004889.ref037","article-title":"Die neue WHO-Klassifikation der malignen Lymphome: Endlich eine weltweit akzeptierte Einteilung","volume":"96","author":"W Hiddemann","year":"1999","journal-title":"Deutsches \u00c4rzteblatt"},{"key":"pmed.1004889.ref038","doi-asserted-by":"crossref","first-page":"103237","DOI":"10.1016\/j.artint.2020.103237","article-title":"Swarm intelligence for self-organized clustering","volume":"290","author":"MC Thrun","year":"2021","journal-title":"Artif Intell"},{"key":"pmed.1004889.ref039","doi-asserted-by":"crossref","first-page":"101093","DOI":"10.1016\/j.mex.2020.101093","article-title":"Uncovering high-dimensional structures of projections from dimensionality reduction methods","volume":"7","author":"MC Thrun","year":"2020","journal-title":"MethodsX"},{"key":"pmed.1004889.ref040","first-page":"225","author":"A Ultsch","year":"2003","journal-title":"Maps for the visualization of high-dimensional data spaces. Workshop on Self organizing Maps (WSOM"},{"issue":"3","key":"pmed.1004889.ref041","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41060-021-00264-2","article-title":"Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)","volume":"12","author":"MC Thrun","year":"2021","journal-title":"Int J Data Sci Anal"},{"key":"pmed.1004889.ref042","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jbi.2016.12.011","article-title":"Machine-learned cluster identification in high-dimensional data","volume":"66","author":"A Ultsch","year":"2017","journal-title":"J Biomed Inform"},{"key":"pmed.1004889.ref043","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patcog.2018.10.026","article-title":"To cluster, or not to cluster: an analysis of clusterability methods","volume":"88","author":"A Adolfsson","year":"2019","journal-title":"Pattern Recogn"},{"key":"pmed.1004889.ref044","article-title":"Improving the sensitivity of statistical testing for clusterability with mirrored-density plot.","author":"MC Thrun","year":"2020","journal-title":"Machine learning methods in visualisation for big data. Norrk\u00f6ping, Sweden: The Eurographics Association"},{"issue":"11","key":"pmed.1004889.ref045","doi-asserted-by":"crossref","first-page":"642","DOI":"10.3390\/bioengineering9110642","article-title":"A Bioinformatics view on acute myeloid leukemia surface molecules by combined bayesian and ABC analysis","volume":"9","author":"MC Thrun","year":"2022","journal-title":"Bioengineering (Basel)"},{"issue":"1","key":"pmed.1004889.ref046","doi-asserted-by":"crossref","first-page":"18988","DOI":"10.1038\/s41598-021-98126-1","article-title":"Distance-based clustering challenges for unbiased benchmarking studies","volume":"11","author":"MC Thrun","year":"2021","journal-title":"Sci Rep"},{"issue":"7","key":"pmed.1004889.ref047","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1038\/s41375-022-01620-2","article-title":"The 5th edition of the World Health Organization classification of haematolymphoid tumours: lymphoid neoplasms","volume":"36","author":"R Alaggio","year":"2022","journal-title":"Leukemia"},{"issue":"20","key":"pmed.1004889.ref048","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1182\/blood-2016-01-643569","article-title":"The 2016 revision of the World Health Organization classification of lymphoid neoplasms","volume":"127","author":"SH Swerdlow","year":"2016","journal-title":"Blood"},{"issue":"2","key":"pmed.1004889.ref049","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.3390\/curroncol30020148","article-title":"Prediction of clinical outcomes with explainable artificial intelligence in patients with chronic lymphocytic leukemia","volume":"30","author":"J Hoffmann","year":"2023","journal-title":"Curr Oncol"},{"key":"pmed.1004889.ref050","unstructured":"National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: chronic lymphocytic leukemia\/small lymphocytic lymphoma. Plymouth Meeting, PA: National Comprehensive Cancer Network; 2023."},{"key":"pmed.1004889.ref051","unstructured":"National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: hairy cell leukemia. Plymouth Meeting, PA: National Comprehensive Cancer Network; 2022."},{"issue":"1","key":"pmed.1004889.ref052","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"1","key":"pmed.1004889.ref053","doi-asserted-by":"crossref","DOI":"10.18637\/jss.v077.i01","article-title":"ranger: a fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"MN Wright","year":"2017","journal-title":"J Stat Soft"},{"issue":"2","key":"pmed.1004889.ref054","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"H Zou","year":"2005","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"1","key":"pmed.1004889.ref055","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"J Friedman","year":"2010","journal-title":"J Stat Softw"},{"key":"pmed.1004889.ref056","doi-asserted-by":"crossref","first-page":"193947","DOI":"10.1155\/2009\/193947","article-title":"FlowFP: a bioconductor package for fingerprinting flow cytometric data","volume":"2009","author":"WT Rogers","year":"2009","journal-title":"Adv Bioinformatics"},{"issue":"12","key":"pmed.1004889.ref057","doi-asserted-by":"crossref","first-page":"100372","DOI":"10.1016\/j.patter.2021.100372","article-title":"New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy","volume":"2","author":"E Greene","year":"2021","journal-title":"Patterns (N Y)"},{"issue":"12","key":"pmed.1004889.ref058","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1002\/cyto.a.22209","article-title":"RchyOptimyx: cellular hierarchy optimization for flow cytometry","volume":"81","author":"N Aghaeepour","year":"2012","journal-title":"Cytometry A"},{"issue":"3","key":"pmed.1004889.ref059","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1038\/nmeth.2365","article-title":"Critical assessment of automated flow cytometry data analysis techniques","volume":"10","author":"N Aghaeepour","year":"2013","journal-title":"Nat Methods"},{"issue":"1","key":"pmed.1004889.ref060","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/cyto.a.22732","article-title":"A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes","volume":"89","author":"N Aghaeepour","year":"2016","journal-title":"Cytometry A"},{"issue":"7","key":"pmed.1004889.ref061","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1002\/cyto.a.22625","article-title":"FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data","volume":"87","author":"S Van Gassen","year":"2015","journal-title":"Cytometry A"},{"issue":"1","key":"pmed.1004889.ref062","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Topic Group \u2018Evaluating diagnostic tests and prediction models\u2019 of the STRATOS initiative","year":"2019","journal-title":"BMC Med"},{"key":"pmed.1004889.ref063","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil A, Caruana R. Predicting good probabilities with supervised learning. In: Proceedings of the 22nd international conference on Machine learning - ICML \u201905, 2005. 625\u201332. https:\/\/doi.org\/10.1145\/1102351.1102430","DOI":"10.1145\/1102351.1102430"},{"issue":"1","key":"pmed.1004889.ref064","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/s41467-019-08987-4","article-title":"Unmasking Clever Hans predictors and assessing what machines really learn","volume":"10","author":"S Lapuschkin","year":"2019","journal-title":"Nat Commun"},{"issue":"1","key":"pmed.1004889.ref065","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/leu.2012.216","article-title":"Improving efficiency and sensitivity: European Research Initiative in CLL (ERIC) update on the international harmonised approach for flow cytometric residual disease monitoring in CLL","volume":"27","author":"AC Rawstron","year":"2013","journal-title":"Leukemia"},{"issue":"12","key":"pmed.1004889.ref066","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.3109\/10428194.2015.1030642","article-title":"Expression patterns of CD200 and CD148 in leukemic B-cell chronic lymphoproliferative disorders and their potential value in differential diagnosis","volume":"56","author":"L Fan","year":"2015","journal-title":"Leuk Lymphoma"},{"issue":"10","key":"pmed.1004889.ref067","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1016\/j.leukres.2011.04.006","article-title":"Use of the receptor tyrosine kinase-like orphan receptor 1 (ROR1) as a diagnostic tool in chronic lymphocytic leukemia (CLL)","volume":"35","author":"S Uhrmacher","year":"2011","journal-title":"Leuk Res"},{"issue":"8","key":"pmed.1004889.ref068","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1182\/blood.V74.8.2718.2718","article-title":"An epitope of the transferrin receptor is exposed on the cell surface of high-grade but not low-grade human lymphomas","volume":"74","author":"L Esserman","year":"1989","journal-title":"Blood"},{"key":"pmed.1004889.ref069","author":"RO Duda","year":"2001","journal-title":"Pattern Classification"},{"issue":"1","key":"pmed.1004889.ref070","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/s41398-021-01224-x","article-title":"How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection","volume":"11","author":"M Jacobs","year":"2021","journal-title":"Transl Psychiatry"},{"key":"pmed.1004889.ref071","doi-asserted-by":"crossref","unstructured":"Bussone A, Stumpf S, O\u2019Sullivan D. The role of explanations on trust and reliance in clinical decision support systems. In: 2015 International Conference on Healthcare Informatics, 2015. pp. 160\u20139. https:\/\/doi.org\/10.1109\/ichi.2015.26","DOI":"10.1109\/ICHI.2015.26"},{"key":"pmed.1004889.ref072","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","article-title":"Explanation in artificial intelligence: insights from the social sciences","volume":"267","author":"T Miller","year":"2019","journal-title":"Artif Intell"},{"issue":"14","key":"pmed.1004889.ref073","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1001\/jama.2022.3580","article-title":"Preparing clinicians for a clinical world influenced by artificial intelligence","volume":"327","author":"CA James","year":"2022","journal-title":"JAMA"},{"key":"pmed.1004889.ref074","doi-asserted-by":"crossref","unstructured":"Thrun M, Pape F, Ultsch A. Interactive machine learning tool for clustering in visual analytics. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020. pp. 479\u201387. https:\/\/doi.org\/10.1109\/dsaa49011.2020.00062","DOI":"10.1109\/DSAA49011.2020.00062"},{"key":"pmed.1004889.ref075","first-page":"7","article-title":"Visualization and 3D printing of multivariate data of biomarkers.","author":"MC Thrun","year":"2016","journal-title":"International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG); Plzen. Czech Republic."},{"key":"pmed.1004889.ref076","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-658-20540-9","author":"MC Thrun","year":"2018","journal-title":"Projection based clustering through self-organization and swarm intelligence. Heidelberg: Springer"},{"key":"pmed.1004889.ref077","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/978-3-031-85870-3_20","article-title":"An efficient multicore CPU implementation of the databionic swarm.","author":"Q Stier","year":"2025","journal-title":"Studies in classification, data analysis, and knowledge organization. Springer Nature Switzerland"},{"key":"pmed.1004889.ref078","doi-asserted-by":"crossref","unstructured":"Ultsch A, Thrun MC. Credible visualizations for planar projections. In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), 2017. pp. 1\u20135. https:\/\/doi.org\/10.1109\/wsom.2017.8020010","DOI":"10.1109\/WSOM.2017.8020010"}],"updated-by":[{"DOI":"10.1371\/journal.pmed.1004889","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000}}],"container-title":["PLOS Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pmed.1004889","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T17:50:39Z","timestamp":1785347439000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pmed.1004889"}},"subtitle":[],"editor":[{"given":"Nic Gabriel","family":"Reitsam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":78,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,13]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pmed.1004889","relation":{},"ISSN":["1549-1676"],"issn-type":[{"value":"1549-1676","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]}}}