Artificial intelligence in early screening for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) remains a significant global health burden with high incidence and mortality rates, particularly in developing regions. Early detection is crucial for improving patient survival, yet conventional screening methods such as endoscopy and non-endoscopic techniques face limitations in accuracy, cost, and dependency on clinician expertise. This review explores the transformative role of artificial intelligence (AI) in ESCC screening. AI technologies, including machine learning, deep learning, and transfer learning, demonstrate remarkable potential for early ESCC screening by targeting high-risk populations, optimizing screening modalities, refining screening intervals, and enhancing cost-effectiveness. AI-driven systems improve lesion detection, vascular pattern recognition, and risk prediction by integrating imaging, genomic, and clinical data. Additionally, AI applications in liquid biopsy analysis enable non-invasive detection of circulating tumor cells and DNA, further advancing early diagnosis. Despite these advancements, challenges such as dataset variability, model generalizability, algorithm transparency, and ethical and legal concerns require resolution to fully harness AI's capabilities. This paper highlights the current applications, persistent challenges, and future directions for AI in revolutionizing ESCC screening.