Esophageal squamous cell carcinoma (ESCC) has high risks of postoperative recurrence, complications, and prolonged nutritional and functional recovery, while conventional follow-up (scheduled visits with imaging, endoscopy, and laboratory testing) is often limited by delays and resource constraints. This review summarizes recent applications of artificial intelligence (AI) across perioperative ESCC care, with emphasis on postoperative surveillance and management. Following PubMed/MEDLINE, etc. were searched (inception–2025) for English-language studies using machine learning, deep learning, radiomics, natural language processing (NLP), and digital health algorithms in postoperative monitoring, recurrence prediction, complication warning, and remote follow-up. Evidence indicates that AI-enabled multimodal models integrating electronic health records, imaging radiomics, and biomarkers can predict major complications (e.g., anastomotic leak and pneumonia) with improved timeliness, enabling earlier intervention compared with symptom-triggered workflows. Imaging-driven radiomics combined with machine learning demonstrates robust performance for recurrence risk and recurrence-pattern prediction, supporting refined risk stratification beyond TNM staging and informing individualized surveillance intensity and adjuvant decision-making. Explainable approaches (e.g., SHAP) enhance clinical interpretability by identifying key predictors such as nutritional and inflammatory indices. Intelligent follow-up systems incorporating NLP, wearable sensors, and electronic patient-reported outcomes (ePROs) facilitate closed-loop monitoring, improve early issue detection, and strengthen patient–clinician communication.
Li et al. (Wed,) studied this question.