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The Journal "Obstetrics and Gynecology"Journal HistoryAims and ScopePolicies on Conflict of Interest, Human and Animal Rights, and Informed ConsentEditorial ProcessData sharing statementAdvertising PolicyThe Process for Handling Cases Requiring Corrections, Retractions, and Editorial Expressions of ConcernEditorial BoardEditorial CounsilInformation for ProfessionalsNewsContacts
ArchiveEthical StandardsSubscription
For authors
InstructionsInformed consent policyContract offerEASE GuidelinesICJME CriteriaWAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications
Reviewing
Procedure for reviewingReviewers 2023-2024
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Obstetrics and Gynegology2025№8
Artificial intelligence technologies in gynecology

Artificial intelligence technologies in gynecology

DOI
https://dx.doi.org/10.18565/aig.2025.92

Mozes V.G., Kotov R.M., Rudaeva E.V., Elgina S.I., Mozes K.B., Vavin G.V.

1) Kemerovo State University, Kemerovo, Russia; 2) Kemerovo State Medical University, Ministry of Health of Russia, Kemerovo, Russia; 3) Kuzbass Regional Clinical Hospital named after S.V. Belyaev, Kemerovo, Russia

The review includes the scientific data from national and foreign studies, 90% of which were published within the last five years. Their topic is related to the testing and analysis of the effectiveness of artificial intelligence (AI) in the diagnosis, treatment and prevention of gynecological pathology. It was concluded that the effectiveness of AI algorithms in many aspects surpasses experts in the diagnosis of cervical cancer, endometrial cancer, ovarian cancer and endometriosis, namely, in evaluating the results of direct and indirect imaging. The literature data show that the integration of AI into clinical practice significantly reduces diagnostic time and is very promising. At the same time, the work of AI in assessing cytology and histology has shown contradictory results. In cervical cytology, AI has surpassed specialists, but in the case of histology of cervical cancer, the results obtained so far do not allow for AI to be fully integrated into clinical practice.
Large language models (LLM) offer significant potential for patients and doctors, with ChatGPT being a primary example. Today, the chatbot often provides correct answers to questions from patients seeking gynecological advice. However, the accuracy of its responses when the questions are more specific and detailed is still not sufficient.
The integration of professional activities with AI-based management systems may reduce the error rate in clinical practice, but their widespread implementation is still limited.
Conclusion: The review demonstrates significant progress in the application of AI in gynecology, especially in the diagnosis of cervical, endometrial, ovarian, and endometriosis cancers. AI algorithms show high effectiveness in analyzing medical images, often surpassing traditional methods in accuracy and speed. However, the use of AI faces several ethical, legal, and practical challenges, such as transparency of decisions, responsibility for mistakes, and integration into clinical practice. Despite this, the potential of AI to improve diagnosis and optimize the work of an obstetrician-gynecologist is obvious.

Authors’ contributions: Kotov R.M. – developing the concept and design of the study; Elgina S.I., Rudaeva E.V., Vavin G.V. – collecting and processing the material; Moses V.G. – writing the text; Moses K.B. – editing the article.
Conflicts of interest: Authors declare lack of the possible conflicts of interest.
Funding: The study was conducted without sponsorship.
For citation: Mozes V.G., Kotov R.M., Rudaeva E.V., Elgina S.I., Mozes K.B., Vavin G.V. 
Artificial intelligence technologies in gynecology.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (8): 16-25 (in Russian)
https://dx.doi.org/10.18565/aig.2025.92

Keywords

artificial intelligence
machine learning
ovarian cancer
cervical cancer
endometrial cancer
endometriosis
Full text (in Russian)

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Received 04.04.2025

Accepted 12.08.2025

About the Authors

Vadim G. Mozes, Dr. Med. Sci., Professor, Director of the Medical Institute, Kemerovo State University, 650000, Kemerovo region – Kuzbass, Kemerovo, Krasnaya str., 6, +7(904)573-24-43, vadimmoses@gmail.com, https://orcid.org/0000-0002-3269-9018
Roman M. Kotov, Dr. Sci. (Econ.), Vice-Rector for Digital Transformation, Kemerovo State University, 650000, Kemerovo region – Kuzbass, Kemerovo, Krasnaya str., 6, +7(3842)58-05-90, kotov@kemsu.ru, https://orcid.org/0000-0003-0238-3466
Elena V. Rudaeva, PhD, Associate Professor, Department of Obstetrics and Gynecology named after G.A. Ushakova, Kemerovo State Medical University, Ministry of Health
of Russia, 650056, Russia, Kemerovo region – Kuzbass, Kemerovo, Voroshilova str., 22a, +7(3842)73-48-56, rudaevae@mail.ru, https://orcid.org/0000-0002-6599-9906
Svetlava I. Elgina, Dr. Med. Sci., Professor, Department of Obstetrics and Gynecology named after G.A. Ushakova, Kemerovo State Medical University, Ministry of Health
of Russia, 650056, Russia, Kemerovo region – Kuzbass, Kemerovo, Voroshilova str., 22a, +7(3842)73-48-56, elginas.i@mail.ru, https://orcid.org/0000-0002-6966-2681
Kira B. Mozes, Teaching Assistant, Department of Polyclinic Therapy and Nursing, Kemerovo State Medical University, Ministry of Health of Russia, 650056, Russia,
Kemerovo region – Kuzbass, Kemerovo, Voroshilova str., 22a, +7(950)276-11-85, kbsolo@mail.ru, https://orcid.org/0000-0003-2906-6217
Grigory V. Vavin, Deputy Chief Physician for Laboratory Diagnostics, Kuzbass Regional Clinical Hospital named after S.V. Belyaev, 650066, Russia, Kemerovo region – Kuzbass, Kemerovo, Oktyabrsky Ave., 22, +7(961)710-55-50, okb-lab@yandex.ru, https://orcid.org/0000-0003-0179-0983
Corresponding author: Elena V. Rudaeva, rudaevae@mail.ru

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