ReviewPathobiology : journal of immunopathology, molecular and cellular biology2024
Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.
Review in Pathobiology : journal of immunopathology, molecular and cellular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 19 citations in OpenAlex.
- Digital Pathology in Hematopathology: From Vision to Deployment.International journal of laboratory hematology · 2026Review
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations.Diagnostics (Basel, Switzerland) · 2026Review
- Robotics-assisted automated staining of bone marrow biopsies: addressing the inconsistency in histological diagnosis of hairy cell leukemia variants.Annals of medicine and surgery (2012) · 2026Article
- Review
- Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.Frontiers in medicine · 2026Article
- Prefibrotic myelofibrosis: recent updates in diagnosis, prognostication, and management 2026.Therapeutic advances in hematology · 2026Review
- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Bone histology for forensic anthropology: a technical review on the advances in microstructural analysis of taphonomically altered buried or subaerially exposed bone.International journal of legal medicine · 2025Review
- Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation.Hematology reports · 2025Review
- How I diagnose and treat patients in the pre-fibrotic phase of primary myelofibrosis (pre-PMF) - practical approaches of a German expert panel discussion in 2024.Annals of hematology · 2025Article
- Valuing diagnostic AI: a structured reimbursement model for learning healthcare systems.Frontiers in digital health · 2025Article
- Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome.Bioengineering (Basel, Switzerland) · 2024Article
- Quantitative interpretation of bone marrow biopsies in MPN-What's the point in a molecular age?British journal of haematology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.
Indexed as
Identifiers
What OpenQuestion holds
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.