ArticleCureus2025
Leveraging Artificial Intelligence in Breast Cancer Screening and Diagnosis.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Radiomics for predicting sensitivity to neoadjuvant chemotherapy in osteosarcoma: current status and advances.Oncology reviews · 2025Pooled it
- A multi-scale hybrid ResNet-transformer with distance-aware learning for interpretable BI-RADS mammographic classification.Scientific reports · 2026Article
- Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk.Diagnostics (Basel, Switzerland) · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer remains the most prevalent malignancy worldwide, posing a significant public health burden due to its high incidence and mortality rates. Early detection through mammography has been instrumental in reducing breast cancer-related deaths; however, traditional screening methods are constrained by human limitations, including variability in interpretation and resource-intensive workflows. Artificial intelligence (AI) has emerged as a transformative tool in breast cancer diagnostics, leveraging machine learning (ML) and deep learning (DL) algorithms to enhance accuracy, efficiency, and accessibility. AI applications in digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound, and magnetic resonance imaging (MRI) have demonstrated improved sensitivity and specificity, reducing false positives and false negatives while optimizing radiologist workload. Despite these advancements, challenges such as data accessibility, algorithm biases, regulatory constraints, and clinical integration hinder widespread AI adoption. Addressing these limitations requires standardized validation protocols, enhanced interpretability through explainable AI (XAI), and improved clinician and patient education. This editorial explores the evolving role of AI in breast cancer screening and diagnosis, emphasizing its potential to bridge healthcare disparities and improve global breast cancer outcomes.
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.