ReviewFrontiers in oncology2026
Multidimensional decision support by artificial intelligence across the full workflow of endoscopic submucosal dissection for early gastric cancer.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Early diagnosis and treatment are essential for improving the prognosis of patients with gastric cancer. Endoscopic submucosal dissection (ESD) is the preferred minimally invasive treatment for early gastric cancer (EGC). Yet the entire diagnostic and therapeutic workflow depends heavily on the physician's experience and is subject to strong subjectivity, procedural variability, and a high rate of missed diagnoses. Artificial intelligence (AI) offers a new approach to addressing these clinical pain points. This review systematically examines the progress of multidimensional AI decision support across the full ESD workflow for EGC. It evaluates the applicable scenarios and limitations of distinct technical pathways, discusses the feasibility of cross-stage integration of AI systems, and analyses the core barriers to clinical translation, including the scarcity of multicentre standardised datasets, limited model interpretability, and poor integration with clinical workflows. Finally, the review anticipates future directions, such as multimodal data fusion and the combination of edge computing with augmented reality technologies. AI is driving ESD diagnosis and treatment towards greater precision and intelligence. The development of an intelligent clinical decision support system (CDSS) that integrates diagnosis, treatment and follow-up may further empower endoscopists to deliver individualised precision therapy for early gastric cancer.
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.