AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health

Esophageal cancer (EC) remains one of the leading causes of cancer-related mortality worldwide. Accurate staging, treatment planning, and prognostic assessment are essential for improving clinical management and patient outcomes. In recent years, artificial intelligence (AI) approaches integrating clinicopathological, imaging, and genomic data have shown considerable potential in these areas. Over the past two years, research in this field has advanced rapidly, supported by the growing availability of large datasets and increasing adoption of multicenter external validation. Recent studies suggest that AI can improve real-time diagnosis and enhance the prediction of treatment response in patients with EC. This review summarizes recent advances in AI applications for esophageal cancer, discusses current challenges, and highlights future directions for research and clinical implementation.