Evidence map›Paper›PMID 39809357›Full record

ReviewCancer letters2025

Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.

Ganji Purnachandra Nagaraju, Tatekalva Sandhya, Mundla Srilatha, Swapna Priya Ganji, Madhu Sudhana Saddala, Bassel F El-Rayes

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. 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.

Ganji Purnachandra NagarajuSchool of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
Tatekalva SandhyaDepartment of Computer Science, Sri Venkateswara University, Tirupati, 517502, AP, India.
Mundla SrilathaDepartment of Biotechnology, Sri Venkateswara University, Tirupati, 517502, AP, India.
Swapna Priya GanjiSchool of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA.
Madhu Sudhana SaddalaBioinformatics, Genomics and Proteomics, University of California, Irvine, Los Angeles, 92697, USA.
Bassel F El-RayesSchool of Medicine, Division of Hematology and Oncology, University of Alabama at Birmingham, Birmingham, AL, 35233, USA. Electronic address: belrayes@uabmc.edu.

Funding

XRAY CRYSTALLOGRAPHYP30CA013148 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Omer Jamy · 1985 to 2026
$165.9M
NCI NIH HHS P30 CA013148
6 · The paper itself

Abstract

GI (Gastrointestinal) malignancies are one of the most common and lethal cancers globally. The dawn of precision medicine and developing technologies have reduced the mortality rates for GI malignancies, underscoring the main role of early detection methods for survival rate improvement. Artificial intelligence (AI) is a new technology that may improve GI cancer screening, treatment, and therapeutic efficiency for better patient care. AI could accelerate the development of targeted therapies by analyzing considerable data from the genome and identifying biomarkers connected with GI tumors. This opens up new avenues toward more tailored and personalized approaches, raising efficacy while reducing undesired side effects. For instance, AI may improve treatment outcomes by accurately predicting patient responses to therapeutic regimens, helping oncologists choose the most effective treatment options. This review will outline the transformative potential of AI in GI oncology by emphasizing the incorporation of AI-based technologies to enhance patient care.

Indexed as

Artificial IntelligenceGastrointestinal NeoplasmsBiomarkers, TumorHumansPrecision MedicinePrognosisBiomarkers, TumorArtificial intelligenceBiomarkersDiagnosisGastrointestinal cancerTherapy

Identifiers

PMID39809357
PMCPMC12677147

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.