Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology

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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Publication Details

Journal
Clinical Obstetrics & Gynecology
Published
2026-01-06
DOI
https://doi.org/10.1097/grf.0000000000000996
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology

Roxanna Haghighat, S. Levi, Melissa K. Frey
Clinical Obstetrics & Gynecology
Artificial Intelligence in Healthcare and Education
article

Artificial Intelligence for Genetic Cancer Risk Assessment in Gynecologic Oncology

Roxanna Haghighat, S. Levi, Melissa K. Frey
article en

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.

Clinical Obstetrics & GynecologyVol. 69(1)
Tufts University (US), Cornell University (US), Presbyterian Hospital (US)
Quality Education
Openalex Percentile: Top 95%
Artificial Intelligence in Healthcare and Education
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