Artificial intelligence technologies in gynecology

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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.

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Sobre autores

Vadim Mozes

Kemerovo State University

Email: vadimmoses@gmail.com
ORCID ID: 0000-0002-3269-9018

Dr. Med. Sci., Professor, Director of the Medical Institute

Rússia, 6, Krasnaya St., Kemerovo, 650000

Roman Kotov

Kemerovo State University

Email: kotov@kemsu.ru
ORCID ID: 0000-0003-0238-3466

Dr. Sci. (Econ.), Vice-Rector for Digital Transformation

Rússia, 6, Krasnaya St., Kemerovo, 650000

Elena Rudaeva

Kemerovo State Medical University

Autor responsável pela correspondência
Email: rudaevae@mail.ru
ORCID ID: 0000-0002-6599-9906

PhD, Associate Professor at the Ushakova Department of Obstetrics and Gynecology

Rússia, 22a, Voroshilov St., Kemerovo, 650056

Svetlava Elgina

Kemerovo State Medical University

Email: elginas.i@mail.ru
ORCID ID: 0000-0002-6966-2681

Dr. Med. Sci., Professor at the Ushakova Department of Obstetrics and Gynecology

Rússia, 22a, Voroshilov St., Kemerovo, 650056

Kira Mozes

Kemerovo State Medical University

Email: kbsolo@mail.ru
ORCID ID: 0000-0003-2906-6217

Teaching Assistant at the Department of Polyclinic Therapy and Nursing

Rússia, 22a, Voroshilov St., Kemerovo, 650056

Grigory Vavin

Belyaev Kuzbass Regional Clinical Hospital

Email: okb-lab@yandex.ru
ORCID ID: 0000-0003-0179-0983

PhD, Deputy Chief Physician for Laboratory Diagnostics

Rússia, 22, Oktyabrsky Ave., Kemerovo, 650066

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