ReviewArchives of computational methods in engineering : state of the art reviews2022
A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.
Review in Archives of computational methods in engineering : state of the art reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 5 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
59 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.Journal of patient-reported outcomes · 2024Pooled it
- Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.European journal of hospital pharmacy : science and practice · 2024Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- A nine-gene diagnostic model for IgA nephropathy based on multi-cohort machine learning: integrating gene expression and immunohistochemical validation.Renal failure · 2026Article
- Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.BMC medical imaging · 2026Article
- Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Review
- Multistage feature selection and stacked generalization model for cancer detection.Scientific reports · 2025Article
- Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.Scientific reports · 2025Article
- Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.JMIR formative research · 2025Article
- De-Escalating Anticancer Treatment: Watch Your Step.Cancers · 2025Review
- Application of machine learning for early detection of chronic diseases in Africa.Journal of public health research · 2025Article
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Survivability prognosis of lung cancer patients with comorbidities-a Gaussian Bayesian network model.Medical & biological engineering & computing · 2025Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries.Discover oncology · 2025Review
- Transforming Cancer Care: The Impact of AI-driven Strategies.Current cancer drug targets · 2025Review
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
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.
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.