Evidence map›Paper›PMID 41114095›Full record

ReviewWorld journal of gastrointestinal oncology2025

Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.

Zhe Wang, Rui-Ying Zhang, Cheng Ji, Jia-Yi Zhang, Bing-Tong Yue, Feng Wang

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhe WangDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China.
Rui-Ying ZhangDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China.
Cheng JiDepartment of Clinical Medicine, North Henan Medical University, Xinxiang 455000, Henan Province, China.
Jia-Yi ZhangDepartment of Clinical Medicine, The First Clinical Medical College of Zhengzhou University, Zhengzhou 45000, Henan Province, China.
Bing-Tong YueDepartment of Clinical Medicine, The First Clinical Medical College of Zhengzhou University, Zhengzhou 45000, Henan Province, China.
Feng WangDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China. zzuwangfeng@zzu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Artificial intelligenceClinical trialsGastrointestinal cancerPatient stratificationReal-world evidence

Identifiers

PMID41114095
PMCPMC12531783

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.