ReviewCurrent drug discovery technologies2024
Artificial Intelligence for the Management of Breast Cancer: An Overview.
Review in Current drug discovery technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- An NLP-Driven Framework for Automated Radiology-Pathology Concordance Assessment in Breast Biopsy.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes.
Indexed as
Identifiers
38047362What 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.