Artificial intelligence in the diagnosis and management of gynecologic cancer

Gynecologic cancers affect over 1.2 million women globally each year. Early diagnosis and effective treatment are essential for improving patient outcomes, yet traditional diagnostic methods often encounter limitations, particularly in low-resource settings. Artificial intelligence (AI) has emerged as a transformative tool that enhances accuracy and efficiency across various aspects of gynecologic oncology, including screening, diagnosis, and treatment. This review examines the current applications of AI in gynecologic cancer care, focusing on areas such as early detection, imaging, personalized treatment planning, and patient monitoring. Based on an analysis of 75 peer-reviewed articles published between 2017 and 2024, we highlight AI's contributions to cervical, ovarian, and endometrial cancer management. AI has notably improved early detection, achieving up to 95% accuracy in cervical cancer screening through AI-enhanced Pap smear analysis and colposcopy. For ovarian and endometrial cancers, AI-driven imaging and biomarker detection have enabled more personalized treatment approaches. In addition, AI tools have enhanced precision in robotic-assisted surgery and radiotherapy, and AI-based histopathology has reduced diagnostic variability. Despite these advancements, challenges such as data privacy, bias, and the need for human oversight must be addressed. The successful integration of AI into clinical practice will require careful consideration of ethical issues and a balanced approach that incorporates human expertise. Overall, AI presents significant potential to improve outcomes in gynecologic oncology, particularly in bridging healthcare gaps in resource-limited settings.

Authors

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Publication Details

Journal
International Journal of Gynecology & Obstetrics
Published
2025-04-25
DOI
https://doi.org/10.1002/ijgo.70094
Citations
15
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
20.40

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article

Artificial intelligence in the diagnosis and management of gynecologic cancer

Nadeem R. Abu‐Rustum, Sarikapan Wilailak, Chaiyawut Paiboonborirak
15 citations
International Journal of Gynecology & Obstetrics
Radiomics and Machine Learning in Medical Imaging
20.40
article

Artificial intelligence in the diagnosis and management of gynecologic cancer

Nadeem R. Abu‐Rustum, Sarikapan Wilailak, Chaiyawut Paiboonborirak
article en
15 citations

Abstract

Gynecologic cancers affect over 1.2 million women globally each year. Early diagnosis and effective treatment are essential for improving patient outcomes, yet traditional diagnostic methods often encounter limitations, particularly in low-resource settings. Artificial intelligence (AI) has emerged as a transformative tool that enhances accuracy and efficiency across various aspects of gynecologic oncology, including screening, diagnosis, and treatment. This review examines the current applications of AI in gynecologic cancer care, focusing on areas such as early detection, imaging, personalized treatment planning, and patient monitoring. Based on an analysis of 75 peer-reviewed articles published between 2017 and 2024, we highlight AI's contributions to cervical, ovarian, and endometrial cancer management. AI has notably improved early detection, achieving up to 95% accuracy in cervical cancer screening through AI-enhanced Pap smear analysis and colposcopy. For ovarian and endometrial cancers, AI-driven imaging and biomarker detection have enabled more personalized treatment approaches. In addition, AI tools have enhanced precision in robotic-assisted surgery and radiotherapy, and AI-based histopathology has reduced diagnostic variability. Despite these advancements, challenges such as data privacy, bias, and the need for human oversight must be addressed. The successful integration of AI into clinical practice will require careful consideration of ethical issues and a balanced approach that incorporates human expertise. Overall, AI presents significant potential to improve outcomes in gynecologic oncology, particularly in bridging healthcare gaps in resource-limited settings.

International Journal of Gynecology & ObstetricsVol. 171(S1)
Memorial Sloan Kettering Cancer Center (US), Cornell University (US), Mahidol University (TH), Bangkok Metropolitan Administration (TH), Ramathibodi Hospital (TH)
Memorial Sloan-Kettering Cancer Center, National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 0%
Radiomics and Machine Learning in Medical Imaging
20.40
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