ReviewDiagnostics (Basel, Switzerland)2022
The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Tumor-Specific Classification of Canine Cutaneous and Subcutaneous Tumors Using Heat-Diffusion Imaging and Artificial Intelligence.Veterinary sciences · 2026Article
- Characterizing the salivary RNA landscape to identify potential diagnostic, prognostic, and follow-up biomarkers for breast cancer.Molecular oncology · 2026Article
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Radiomics-based mammographic abnormality identification via radiologist annotations.BJR artificial intelligence · 2026Article
- Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer.Discover oncology · 2025Article
- Evaluating the Concordance Between ChatGPT and Multidisciplinary Teams in Breast Cancer Treatment Planning: A Study from Bosnia and Herzegovina.Journal of clinical medicine · 2025Article
- Review
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Recent Advancements in Breast Cancer Therapies and Biomarkers: Mechanisms and Clinical Significance.Current pharmaceutical biotechnology · 2025Review
- Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS.Diagnostics (Basel, Switzerland) · 2024Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Clinical and Surgical Applications of Large Language Models: A Systematic Review.Journal of clinical medicine · 2024Review
- Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review.Journal of clinical medicine · 2024Article
- Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection.Cureus · 2024Review
- A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.Journal of magnetic resonance imaging : JMRI · 2024Review
- Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.Frontiers in oncology · 2024Article
- Evaluating ChatGPT as an adjunct for the multidisciplinary tumor board decision-making in primary breast cancer cases.Archives of gynecology and obstetrics · 2023Article
- Training and validation of a novel non-invasive imaging system for ruling out malignancy in canine subcutaneous and cutaneous masses using machine learning in 664 masses.Frontiers in veterinary science · 2023Article
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
Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors' clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide.
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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.