ReviewWorld journal of gastrointestinal oncology2025
Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.
Review in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- TREM2 in glioma: Reprogramming the immune microenvironment from mechanistic understanding to clinical translation (Review).Molecular medicine reports · 2026Review
- A temporal keyword co-occurrence network mining framework for detecting structural transitions in cancer biomarker research (2006-2023).Scientific reports · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Review
- Cancer-associated fibroblasts as architects of acquired immune-privileged-like niches in gastrointestinal cancers: lessons from ocular immune privilege and barrier biology.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal (GI) cancers exact a staggering global toll through high incidence, mortality, and treatment costs, yet clinical research continues to be hampered by inadequate patient stratification, challenging recruitment, suboptimal adherence, and time-consuming endpoint confirmations. Against this backdrop, artificial intelligence (AI) emerges as a powerful game-changer, offering streamlined trial design, predictive enrollment matching, dynamic endpoint assessment, and real-world data integration. This review synthesizes AI-driven advancements across the GI cancer research continuum. It covers precise patient stratification, automated efficacy evaluations, and remote compliance management. The analysis also addresses persistent challenges in data standardization, privacy protection, and regulatory oversight. We underscore the need for synergistic clinician-AI collaboration, alongside robust frameworks that ensure interpretability and ethical deployment. By illuminating the potential of AI to accelerate trial timelines, refine patient selection, and enhance outcome measurement, we aim to inspire new strategies that can significantly reduce the global burden of GI malignancies. Ultimately, this work provides a blueprint for stakeholders seeking to harness AI's transformative capabilities, fostering a future in which GI cancer clinical research becomes more agile, personalized, and impactful for patients and healthcare systems alike.
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.