ReviewCancers2020
Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- AI-Enhanced Quantitative IHC Analysis for Prognostic Stratification in Marginal Zone Lymphoma: Development of a Revised MZL-IPI Model.Diagnostics (Basel, Switzerland) · 2026Article
- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.The veterinary quarterly · 2025Article
- Impact of Magnification, Image Type, and Number on Convolutional Neural Network Performance in Differentiating Canine Large Cell Lymphoma From Non-Lymphoma via Lymph Node Cytology.Veterinary clinical pathology · 2025Article
- Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development.Veterinary clinical pathology · 2025Review
- Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.Annals of medicine and surgery (2012) · 2025Review
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- Transformative Role of Artificial Intelligence in Reporting Haematology Cases: A Case Report.Cureus · 2024Article
- Artificial intelligence-based differential diagnosis of orbital MALT lymphoma and IgG4 related ophthalmic disease using hematoxylin-eosin images.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology.The journal of pathology. Clinical research · 2024Article
- [Chinese expert consensus on the technical and clinical practice specifications of artificial intelligence assisted morphology examination of blood cells (2024)].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2024Article
- Advancements in technology for characterizing the tumor immune microenvironment.International journal of biological sciences · 2024Review
- Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.Pathobiology : journal of immunopathology, molecular and cellular biology · 2024Review
- Development of an automated estimation of foot process width using deep learning in kidney biopsies from patients with Fabry, minimal change, and diabetic kidney diseases.Kidney international · 2024Article
- Artificial intelligence feasibility in veterinary medicine: A systematic review.Veterinary world · 2023Article
- Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence and digital pathology: clinical promise and deployment considerations.Journal of medical imaging (Bellingham, Wash.) · 2023Review
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
- An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023Article
- Phenotyping senescent mesenchymal stromal cells using AI image translation.Current research in biotechnology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
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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.