ArticleArtificial intelligence review2022
The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.
Article in Artificial intelligence review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 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
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis.Asian Pacific journal of cancer prevention : APJCP · 2023Pooled it
- Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy.Surgical endoscopy · 2023Pooled it
- Pooled it
- Deep Learning-Based Prediction System for Surgical Difficulty in Rectal Cancer Patients Using MRI Pelvimetry.Yonsei medical journal · 2026Article
- Integrating histopathology and immune marker analysis for machine learning-based colorectal cancer prognostics.Diagnostic pathology · 2026Article
- Predicting liver metastasis in colorectal cancer patients using routine biochemical tests enhanced by machine learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Multistage feature selection and stacked generalization model for cancer detection.Scientific reports · 2025Article
- Navigating the Colorectal Cancer Maze: Unveiling Pathways To Diagnosis, Management, Pathophysiology and Prevention.Current oncology reports · 2025Review
- How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?Annals of coloproctology · 2025Article
- Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap.Current oncology reports · 2025Review
- The diagnostic and prognostic value ofBioImpacts : BI · 2025Article
- Hierarchical Active Learning with Label Proportions on Data Regions.IEEE transactions on knowledge and data engineering · 2024Article
- Harnessing Artificial Intelligence for the Detection and Management of Colorectal Cancer Treatment.Cancer prevention research (Philadelphia, Pa.) · 2024Review
- Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks.BMC medical informatics and decision making · 2024Article
- From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies.International journal of molecular sciences · 2024Review
- An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images.Scientific reports · 2024Article
- Review
- Review
- Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Hierarchical Active Learning With Qualitative Feedback on Regions.IEEE transactions on human-machine systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.
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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.