Purpose: Ovarian cancer is oftendiagnosed late due to vague symptoms, leading to poor survival rate. Improved screening tests could mitigate this issue. This narrative review examines the potential and challenges of integrating artificial intelligence (A.I.) into ovarian cancer screenings, with a focus on improving early detection, diagnosis, and personalized risk assessment.
Method: A comprehensive review of existing literature was conducted, analyzing studies and discussions within the scientific community.
Results: A.I. shows promise in significantly improving the ovarian cancer screening processes, increasing accuracy, efficiency, and resource allocation. However, data quality and bias issues pose considerable challenges, potentially leading to healthcare disparities.
Conclusions: Integrating A.I. into ovarian cancer screenings offers potential benefits but comes with significant challenges. By promoting diverse data collection, engaging with underrepresented groups, and ensuring ethical data use, A.I. can be harnessed for more accurate and equitable ovarian cancer diagnoses.
Keywords: Artificial intelligence; Diagnosis; Machine learning; Ovarian Cancer; Screening.
© 2024. The Author(s), under exclusive licence to Society for Reproductive Investigation.