SynthesisBMJ health & care informatics2024
Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.
Synthesis in BMJ health & care informatics, 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
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.BMJ health & care informatics · 2024Pooled it
- Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.Cancer cell international · 2026Review
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Multi-scale information bottleneck with confidence-weighted decision fusion for robust breast ultrasound lesion classification.Frontiers in oncology · 2026Article
- Machine Learning Models Utilizing Oxidative Stress Biomarkers for Breast Cancer Prediction: Efficacy and Limitations in Sentinel Lymph Node Metastasis Detection.Biomedicines · 2025Article
- Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- MS-Detector: A Hierarchical Deep Learning Method to Detect Muscle Strain Using Bilateral Symmetric Ultrasound Images of the Body.Diagnostics (Basel, Switzerland) · 2025Article
- Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach.JMIR cancer · 2025Article
- Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.Scientific reports · 2025Article
- Clinical Value of Nomogram Model based on Multimodality Ultrasound Image Characteristics Differentiating Benign and Malignant Breast Masses.Current medical imaging · 2025Article
- A novel interpretable and real-time dengue prediction framework using clinical blood parameters with genetic and GAN-based optimization.Frontiers in artificial intelligence · 2025Article
- Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.BMC medical imaging · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms.
methodsIn this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms 'breast cancer', 'explainable', 'interpretable', 'machine learning', 'artificial intelligence' and 'XAI'. Rayyan online platform detected duplicates, inclusion and exclusion of papers.
resultsThis study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans' confidence in using the XAI system-additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer.
conclusionXAI is not conceded to increase users' and doctors' trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO REGISTRATION NUMBER: CRD42023458665.
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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.