ReviewFrontiers in oncology2025
Advancements in the application of artificial intelligence in the field of colorectal cancer.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based computer-aided detection systems for adenomas during colonoscopy: a systematic review and Bayesian network meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Combined water exchange and cap-assisted colonoscopy reduces cecal intubation time and patient discomfort in patients with unsedated colonoscopy: a randomized controlled trial.BMC gastroenterology · 2025Trial
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.NPJ digital medicine · 2025Article
- A Novel Microfluidic Platform for Circulating Tumor Cell Identification in Non-Small-Cell Lung Cancer.Micromachines · 2025Article
- Integrating inflammation, nutrition, and artificial intelligence: A new era in colorectal cancer prognostic scoring.World journal of gastroenterology · 2025Article
- Review
- Article
- The Potential Role and Effective Components of Sanyeqing as the Potential Therapeutic Candidates for IBD and CRC.OncoTargets and therapy · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a prevalent malignant tumor in the digestive system. As reported in the 2020 global cancer statistics, CRC accounted for more than 1.9 million new cases and 935,000 deaths, making it the third most common cancer worldwide in terms of incidence and the second leading cause of cancer-related deaths globally. This poses a significant threat to global public health. Early screening methods, such as fecal occult blood tests, colonoscopies, and imaging techniques, are crucial for detecting early lesions and enabling timely intervention before cancer becomes invasive. Early detection greatly enhances treatment possibilities, such as surgery, radiation therapy, and chemotherapy, with surgery being the main approach for treating early-stage CRC. In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. DL, a more advanced form of ML, simulates the brain's processing power, enhancing the accuracy of tumor detection, differentiation, and prognosis predictions. These innovations offer the potential to revolutionize cancer care by boosting diagnostic accuracy, refining treatment approaches, and ultimately enhancing patient outcomes.
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.