Integration of Artificial Intelligence in Endometrial Cancer Management

Endometrial cancer is one of the most frequent gynecologic malignancies in developed countries, with increasing incidence worldwide. The complexity of molecular and clinicopathologic data has highlighted the need for advanced analytical tools to optimize risk assessment and therapeutic decision-making. Artificial intelligence (AI) has emerged as a disruptive technology in oncology, enabling high-dimensional data integration and predictive modeling. Its application in endometrial cancer holds significant potential to improve diagnosis, prognostic stratification, and individualized treatment strategies. This narrative review synthesizes evidence from original research articles, systematic reviews, meta-analyses, and emerging translational studies focusing on AI applications in endometrial cancer. It evaluates AI-driven approaches in screening and early detection, machine learning-based diagnostic and risk stratification models, radiomics and imaging analytics, deep learning applications in histopathology and molecular classification, and predictive algorithms for treatment planning and prognostic assessment. A marked increase in scientific publications over the past decades underscores the expanding role of AI in endometrial cancer research. AI-based systems leveraging multimodal data including clinical variables, imaging radiomics, digital histopathology, and molecular profiling demonstrate improved performance in diagnostic classification, risk prediction, and treatment optimization. Enhanced accuracy in tumor grading, molecular subtype prediction, and survival modeling compared with conventional statistical approaches was reported. AI represents a real opportunity to enhance the comprehensive management of endometrial cancer. Nevertheless, its clinical adoption depends on rigorous external validation, harmonization of datasets, transparency of algorithms, and integration into multidisciplinary decision-making pathways that preserve clinical judgment and ensure equitable access to innovation.