ReviewCancer cell international2021
Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future.
Review in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 4 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
103 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- P300 Event-Related Potentials as Cognitive Biomarkers in Neurological and Neuropsychiatric Disorders: A Systematic Review.Revista de neurologia · 2026Pooled it
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Economic evaluations of screening and case-finding for Chronic Obstructive Pulmonary Disease (COPD): a systematic review.NPJ primary care respiratory medicine · 2026Pooled it
- A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.Frontiers in oncology · 2023Pooled it
- AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.Journal of medical Internet research · 2026Article
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Machine learning application in colon cancer treatment outcome prediction.Scientific reports · 2026Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- Mission imputable: Effects of missing data processing on infectious disease detection and prognosis.PloS one · 2026Article
- Bibliometric analysis of research on artificial İntelligence applications in breast cancer diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2026Article
- Machine Learning for Lymph Node Metastasis Prediction in Early Gastric Cancer: A Comparative Analysis.International journal of medical sciences · 2026Article
- Early diagnosis of nasopharyngeal carcinoma based on machine learning modelling and blood plasma metallomics analysis.Scientific reports · 2025Article
- The application of artificial intelligence in veterinary oncology: a scoping review.BMC veterinary research · 2025Article
- Establishment and validation of a diagnostic model for cholangiocarcinoma based on LightGBM machine-learning algorithm.Scientific reports · 2025Article
- Development of a diagnostic model for ovarian cancer based on machine learning algorithms and functional analysis of key biomarker SOX17.Journal of ovarian research · 2025Article
- Review
- Use of AI Histopathology in Breast Cancer Diagnosis.Medicina (Kaunas, Lithuania) · 2025Review
43 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.
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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.