AI for Early Esophageal Cancer Detection: AJG | July 2026

Introduction:

Early detection of esophageal squamous cell carcinoma (ESCC) is critical for curative endoscopic treatment. However, subtle lesions are often missed during routine endoscopy. This meta-analysis evaluated whether deep learning (AI)-based endoscopic systems can improve the detection of early ESCC.

Why was this study needed?

• Early ESCC remains challenging to detect with conventional endoscopy.
• Diagnostic accuracy varies according to endoscopist experience.
• AI has the potential to improve lesion recognition and reduce missed cancers.
• The comparative performance of AI versus endoscopists remains uncertain.
• Evidence is needed before widespread clinical adoption of AI-assisted endoscopy.

Results:

• AI demonstrated excellent diagnostic accuracy, outperforming junior endoscopists and achieving performance comparable to experienced senior endoscopists for detecting early ESCC.
• AI assistance significantly improved the performance of junior endoscopists and also enhanced the diagnostic accuracy of experienced endoscopists.
• AI performed particularly well in detecting subtle early lesions, although most available evidence comes from retrospective studies conducted in Asian populations.

Clinical Impact:

This meta-analysis supports AI as a valuable clinical decision-support tool rather than a replacement for expert endoscopists. AI may help standardize detection, reduce missed early cancers, and shorten the learning curve for less experienced endoscopists, particularly in high-risk screening programs.

Bottom Line:

AI is not replacing experienced endoscopists—but it is making every endoscopist better. Deep learning significantly improves the detection of early esophageal squamous cell carcinoma, especially among junior endoscopists, and is poised to become an important adjunct to routine endoscopic practice.