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Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology: A Review of the Current Landscape
Roxanna Haghighat1, Sarah R Levi2, Melissa K Frey1,3
1Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY.
Abstract:
Hereditary cancer syndromes are associated with up to 25% of ovarian and 5% of endometrial cancers, yet rates of genetic testing and counseling remain low. Artificial intelligence (AI) offers new opportunities to streamline risk assessment, enhance gene variant interpretation, and expand access to genetic counseling. This narrative review synthesizes current evidence on AI applications in gynecologic cancer genetic risk assessment, including chatbot-based risk assessment, natural language processing of electronic records, and machine-learning approaches to variant classification. We highlight key challenges, including data bias, privacy, and implementation barriers, and outline future directions for AI technologies in gynecologic cancer genetic risk assessment.
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