Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management

Abstract

Ovarian cancer remains one of the deadliest gynecologic malignancies. Poor outcomes largely reflect late diagnosis, marked inter- and intratumoral heterogeneity, and variable treatment response. This review summarizes recent advances in artificial intelligence (AI) for ovarian cancer research and clinical care, focusing on imagine-based radiology, digital pathology; longitudinal clinical data/Electronic Health Record (EHR), and spatial-temporal multi-omics. AI approaches have been applied to tumor detection and classification, prognostic risk stratification, and treatment response prediction. Multimodal models that integrate imaging, molecular profiling, and clinical data enable more refined characterization of tumor heterogeneity and the tumor microenvironment, supporting improved diagnosis, risk assessment, and individualized management.