Future Perspective of AI Diagnostics for Early Barrett's Neoplasia

Background: Barrett’s esophagus (BE) represents the only established precursor to esophageal adenocarcinoma. While endoscopic surveillance is a cornerstone of early detection, it remains limited by interobserver variability, sampling error, and variability in diagnostic yield. In recent years, artificial intelligence (AI) has emerged as a promising tool to improve the detection and characterization of neoplastic lesions in BE. Summary: This review outlines the current landscape and future potential of AI applications in the endoscopic management of BE. Diagnostic systems employing convolutional neural networks and transformer-based architectures have achieved high performance for both lesion detection (CADe) and characterization (CADx), with several models externally validated in multicenter cohorts. The first CE-certified commercial system, CADU, has further marked the entry of AI into clinical use. Emerging developments include AI tools for infiltration depth estimation, vessel detection during endoscopic submucosal dissection, post-therapeutic surveillance, and procedural quality assessment. Challenges related to generalizability, human–AI interaction, ethical implementation, and regulatory compliance are discussed in the context of clinical translation. Key Messages: • AI systems demonstrate high diagnostic accuracy and enable real-time assistance in BE surveillance. • In-domain pretrained models and transformer-based systems may improve robustness and adaptability. • Clinical applications are expanding beyond diagnostics to therapeutic guidance and post-treatment monitoring. • Successful implementation depends on rigorous validation, explainability, and integration into clinical workflows.