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The following is a summary of “Artificial Intelligence in Early Screening for Esophageal Squamous Cell carcinoma,” published in the March 2025 issue of Best Practice & Research Clinical Gastroenterology by Yan et al. 

Esophageal squamous cell carcinoma (ESCC) remains one of the most lethal malignancies worldwide, with a particularly high burden in developing regions where access to timely and accurate diagnostic tools is often limited. Early-stage identification of ESCC significantly improves survival outcomes, yet conventional screening approaches—such as endoscopic examinations and non-endoscopic diagnostic modalities—are hindered by limitations in sensitivity, specificity, cost, and reliance on operator expertise. This review comprehensively examines the emerging and rapidly evolving role of artificial intelligence (AI) in enhancing the detection and early diagnosis of ESCC. AI technologies, including machine learning (ML), deep learning, and transfer learning, have demonstrated substantial promise in redefining the ESCC screening paradigm. 

By integrating and analyzing large-scale clinical, imaging, and molecular datasets, AI-driven systems offer improved lesion identification, vascular pattern assessment, and individualized risk stratification. These capabilities enable AI to aid in optimizing screening protocols—such as refining target populations, adjusting screening intervals, and enhancing overall cost-effectiveness—especially in resource-limited settings. Moreover, the application of AI in interpreting endoscopic imagery has yielded improved detection rates of subtle or early-stage lesions that may otherwise be overlooked by human evaluators. AI is also increasingly used in liquid biopsy analysis, facilitating non-invasive early detection by identifying circulating tumor cells and cell-free DNA associated with ESCC. These approaches offer a valuable supplement—or in some settings, a viable alternative—to conventional invasive procedures. However, the widespread integration of AI into clinical workflows is not without obstacles. Key challenges include the heterogeneity and limited availability of annotated datasets, which can impact model robustness and reproducibility across diverse populations. 

Additionally, concerns regarding algorithm transparency, interpretability, and the “black box” nature of many AI models raise ethical and legal considerations, particularly in clinical decision-making contexts. Regulatory frameworks governing the validation, deployment, and oversight of AI-based tools are still evolving, further complicating their clinical translation. Despite these hurdles, ongoing advancements in computational power, data standardization, and algorithm refinement continue to drive progress. Collaborative efforts between clinicians, data scientists, and regulatory bodies will be critical to ensuring that AI technologies are safely and effectively integrated into ESCC screening programs. In summary, AI holds the transformative potential to revolutionize early ESCC detection by addressing current diagnostic limitations and offering scalable, accurate, and personalized screening solutions. Continued research, validation in diverse clinical settings, and ethical deployment strategies will be essential to fully realize AI’s role in combating this global health challenge.

Source: sciencedirect.com/science/article/pii/S1521691825000319