ReviewBioengineering (Basel, Switzerland)2024
New Frontiers in Breast Cancer Imaging: The Rise of AI.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study.Quantitative imaging in medicine and surgery · 2026Article
- Dynamic contrast-enhanced magnetic resonance imaging-derived three-dimensional tumor volume improves prognostic stratification in breast cancer: a retrospective cohort study.Gland surgery · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Imaging Ductal Carcinoma In Situ in the Era of De-Escalation: Role, Limits, and Clinical Implications for Risk-Adapted Management.Diagnostics (Basel, Switzerland) · 2026Review
- Epidemiology, early detection, and management of breast cancer in China: A comprehensive review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Clinically Focused Computer-Aided Diagnosis for Breast Cancer Using SE and CBAM with Multi-Head Attention.Tomography (Ann Arbor, Mich.) · 2025Article
- Performance of an Artificial Intelligence Support System on Screening Mammography Cases Proceeding to Stereotactic Biopsy.Cancers · 2025Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- Liver Cancer: Artificial Intelligence (AI)-Based Integrated Therapeutic Approaches.Bioengineering (Basel, Switzerland) · 2025Article
- Evolution of an Artificial Intelligence-Powered Application for Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Breast cancer: pathogenesis and treatments.Signal transduction and targeted therapy · 2025Review
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Artificial Intelligence in Breast Cancer Diagnosis and Treatment: Advances in Imaging, Pathology, and Personalized Care.Life (Basel, Switzerland) · 2024Review
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
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.