ArticleWorld journal of gastrointestinal oncology2025
Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations.
Article 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 2 papers, 1 of them a synthesis that pooled it.
What it found
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Efficacy of robot-assisted surgical training in medical education: a systematic review and meta-analysis of randomized controlled trials.BMC surgery · 2025Pooled it
- Micrometastasis and ITCs in OSCC: Integrating AI, Radiomics, and Biomarkers for Clinically Useful Nodal Staging.Head and neck pathology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With gastric cancer ranking among the most prevalent and deadly malignancies worldwide, early detection and individualized prognosis remain essential for improving patient outcomes. This letter discusses recent advancements in artificial intelligence (AI)-driven predictive tools for gastric cancer, emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy. AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology. Additionally, this letter highlights the comparison of these AI tools with traditional methodologies, demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection. Furthermore, this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable, transparent, and unbiased outcomes. Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.
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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 Socratic graph.