ReviewCommunications medicine2022
Artificial intelligence and machine learning in cancer imaging.
Review in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 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
130 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging.Medical & biological engineering & computing · 2025Pooled it
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Accuracy of breast density assessment using artificial intelligence by convolutional neural network for carriers of UGT1A1 polymorphisms with Gilbert's Syndrome - a pilot study.Clinics (Sao Paulo, Brazil) · 2026Article
- Prostate Cancer at the Microbial Crossroads: Illuminating a New Frontier in Precision Medicine.The world journal of men's health · 2026Review
- Reasoning in machine vision by learning fast and slow thinking.Nature communications · 2026Article
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Artificial intelligence in cancer diagnosis and therapy using sex and gender as precision biomarkers.Discover oncology · 2026Review
- Deep Learning-Based Prediction System for Surgical Difficulty in Rectal Cancer Patients Using MRI Pelvimetry.Yonsei medical journal · 2026Article
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Review
- Prediction of lymphovascular invasion in rectal cancer based on multimodal magnetic resonance imaging radiomics model.World journal of gastrointestinal surgery · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- Development of a prognostic model for preoperative stage I-III breast cancer using machine learning with integrated cone-beam breast computed tomography data.La Radiologia medica · 2026Article
- Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.Journal of medical Internet research · 2026Article
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Cervical lymph node response to 131I therapy in differentiated thyroid cancer using radiomics and clinical features.Frontiers in oncology · 2026Article
- MRI and Meningioma Research: Hotspots and Trends via Bibliometric AnalysisCurrent medical imaging · 2026Article
- Quantitative assessment of impact of technical and population-based factors on fairness of AI models for chest X-ray scans.Computers in biology and medicine · 2025Article
70 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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.