Artificial intelligence in ovarian cancers- from diagnosis to treatment; a literature review

Abstract

Ovarian cancer remains the most lethal gynecological malignancy due to challenges in early detection stemming from a lack of reliable biomarkers. Despite this, various laboratory tests are commonly employed in clinical practice, some showing diagnostic and prognostic promise for ovarian cancer. This review aims to synthesize current literature to delineate the role of artificial intelligence (AI) in both the diagnosis—from laboratory tests to imaging—and treatment of ovarian cancers. Thus, the epidemiology, risk factors, pathology, screening methods, as well as the integration of AI in the diagnosis of ovarian cancer (AI based on both blood biomarkers and imaging-based ovarian cancer detection) are presented. AI and biomarkers show considerable potential in improving ovarian cancer management, but ongoing research efforts are necessary to refine these technologies and integrate them effectively into clinical practice. This approach aims to enhance diagnostic accuracy, predict patient outcomes, and ultimately improve treatment strategies for ovarian cancer patients.

Keywords

• ovarian cancer
• management
• artificial intelligence
• diagnosis
• treatment