ArticleFrontiers in immunology2022
Explainable artificial intelligence for precision medicine in acute myeloid leukemia.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.Frontiers in oncology · 2025Pooled it
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Emerging strategies and novel therapeutic targets in acute myeloid leukemia: current advances and future directions.Biomarker research · 2025Review
- Current methods in explainable artificial intelligence and future prospects for integrative physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Choosing the Right Cell Line for Acute Myeloid Leukemia (AML) Research.International journal of molecular sciences · 2023Review
- Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis.BMJ open · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319
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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.