ArticleNature reviews. Cancer2021
Artificial intelligence in cancer research, diagnosis and therapy.
Article in Nature reviews. Cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 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
135 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis.Cancer medicine · 2023Pooled it
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Deep learning CT signature for predicting early liver metastases in pancreatic ductal adenocarcinoma.Nature communications · 2026Observational
- Extracellular vesicles in gastric cancer: biological functions, clinical applications, and engineering strategies.Medical oncology (Northwood, London, England) · 2026Review
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- AI-enabled precision reproductive medicine: a comprehensive review of clinical applications, decision frameworks, and evidence-based implementation.Journal of assisted reproduction and genetics · 2026Review
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Dissecting PANoptosis in the Nervous System: A Unified Cell-Death Mechanism Driving Neuroimmune Activation and Chronic Neuroinflammation.Molecular neurobiology · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Establishment of the China Elderly Comorbidity Medical Database (CECMed) and its application in machine learning-based prediction.BMC geriatrics · 2026Article
- Review
- Crowdsourcing Cancer Survivors' Perspectives on the Use of Artificial Intelligence and Automation in Financial Hardship Interventions.JCO oncology practice · 2026Article
- Engaging Arab/MENA Communities in Learning Health Systems: Insights and Guidance for Future Research on AI and Health Equity.Learning health systems · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- In Silico Drug Design and Discovery: Big Data for Small Molecule Design-2nd Edition.Biomolecules · 2026Article
- An ethics-informed computable audit framework for monitoring misdiagnosis risk in AI-assisted diagnosis.Scientific reports · 2026Article
- Cancer neuroscience: signaling pathways and new therapeutic strategies for cancer.Signal transduction and targeted therapy · 2026Review
- Assessing Large Language Models for Early Article Identification in Otolaryngology-Head and Neck Surgery Systematic Reviews.Health care science · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
75 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
standfirstArtificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.
Indexed as
Identifiers
34535775What 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.