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Gastrointestinal Tumor Research and Treatment / Gastric Cancer Management and Outcomes · Journal article
Frontiers in Oncology · August 17, 2026
A consensus or society position rather than new primary data.
This is a narrative systematic review synthesizing the current state of artificial intelligence applications across the entire endoscopic submucosal dissection workflow for early gastric cancer diagnosis and treatment. The review identifies AI as a promising avenue to reduce procedural variability and missed diagnoses, while highlighting critical barriers to clinical implementation including data standardisation, model interpretability, and workflow integration.
Systematic review. Early gastric cancer patients; endoscopic submucosal dissection diagnostic and therapeutic workflows. Intervention: Artificial intelligence decision support systems across ESD workflow stages.
AI offers potential to address clinical pain points of subjectivity, procedural variability, and missed diagnoses in ESD for early gastric cancer Core barriers to clinical translation include scarcity of multicentre standardised datasets, limited model interpretability, and poor integration with clinical workflows Future directions include multimodal data fusion and combination of edge computing with augmented reality technologies
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This review contextualises the emerging role of AI in supporting the full diagnostic and therapeutic ESD workflow, identifying current limitations and prerequisite developments needed before clinical translation. Clinicians should view AI decision support as a developing tool that requires standardised datasets and workflow integration before widespread adoption.
A systematic review examining the state of AI application across the ESD workflow for early gastric cancer, synthesizing progress, limitations, and future directions to inform clinical translation rather than reporting original clinical trial data.
As stated by the source record.
This review contextualises the emerging role of AI in supporting the full diagnostic and therapeutic ESD workflow, identifying current limitations and prerequisite developments needed before clinical translation. Clinicians should view AI decision support as a developing tool that requires standardised datasets and workflow integration before widespread adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.