ReviewThe British journal of radiology2020
CAD and AI for breast cancer-recent development and challenges.
Review in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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
80 citing papers in PubMed, 1 synthesis or guideline pooled it, 165 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.La Radiologia medica · 2023Trial
- Bladder cancer segmentation using u-net-based deep-learning.Scientific reports · 2026Article
- Effect of a Real-Time Artificial Intelligence-Assisted Ultrasound System on BI-RADS C4 Breast Lesions Based on Breast Density.Cancers · 2026Article
- Predictive deep learning model based on contrast-enhanced mammography for breast cancer diagnosis: a pilot study.BJR artificial intelligence · 2026Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- AI-CAD-Guided Mammographic Assessment of Tumor Size and T Stage: Concordance with MRI for Clinical Staging in Breast Cancer Patients Considered for NAC.Tomography (Ann Arbor, Mich.) · 2025Article
- Assessing the value of artificial intelligence-based image analysis for pre-operative surgical planning of neck dissections and iENE detection in head and neck cancer patients.Discover oncology · 2025Article
- Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.Frontiers in oncology · 2025Article
- Mam-Incept-Net: a novel inception model for precise interpretation of mammography images.PeerJ. Computer science · 2025Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Architecture-Aware Augmentation: A Hybrid Deep Learning and Machine Learning Approach for Enhanced Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Navigating the Spectrum: Assessing the Concordance of ML-Based AI Findings with Radiology in Chest X-Rays in Clinical Settings.Healthcare (Basel, Switzerland) · 2024Article
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.The British journal of radiology · 2024Article
- CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks.Heliyon · 2024Article
- A Review of Artificial Intelligence in Breast Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.Translational cancer research · 2024Article
20 more citing papers are in PubMed but not listed here.
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 at 1 institution in 1 country.
Funding
Abstract
Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.
Indexed as
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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.