ArticlePLOS digital health2023
Explainable AI identifies diagnostic cells of genetic AML subtypes.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 51 citations in OpenAlex.
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Therapeutic Potential of Ginger Rhizomes (Zingiber officinale) on Leukemia.Biotechnology and applied biochemistry · 2026Review
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.Leukemia · 2026Article
- Computer-aided drug design in acute myeloid leukemia: a comprehensive review of advances, challenges, and future prospect.Journal of computer-aided molecular design · 2026Review
- Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples.Scientific reports · 2026Article
- A multi-scale feature fusion of deep learning network for classifying acute leukemia genetic subtypes from blood smear images.Discover oncology · 2026Article
- Acute Myeloid Leukemia subtype classification using single blood cell images.Discover oncology · 2025Article
- Granulocyte abundance and maturation state at diagnosis predicts treatment-free remission in CML.Leukemia · 2025Article
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- A large expert-annotated single-cell peripheral blood dataset for hematological disease diagnostics.Scientific data · 2025Article
- Promises and challenges of artificial intelligence in haematological diagnostics.British journal of haematology · 2025Review
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 2025Review
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient's blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.