ReviewInternational journal of clinical oncology2024
Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.
Review in International journal of clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.Clinical and experimental dental research · 2025Article
- Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.International journal of molecular sciences · 2025Review
- Challenges and opportunities for Aotearoa/New Zealand's breast screening programme.Journal of the Royal Society of New Zealand · 2025Review
- An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.Journal of hepato-biliary-pancreatic sciences · 2025Article
- Feasibility study of single-image super-resolution scanning system based on deep learning for pathological diagnosis of oral epithelial dysplasia.Frontiers in medicine · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- The role of HGH1 in breast cancer prognosis: a study on immune response and cell cycle.BMC cancer · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.
Indexed as
Identifiers
38619651What 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.