Main Article Content

Елена Валерьевна Litvinova
Oksana Vladimirovna Noskova

Abstract

The purpose of the work – researching the potential applications of artificial intelligence in obstetrics and gynecology.
Materials and methods of work. The search for scientific information was conducted using domestic and foreign Internet resources such as Google Scholar, PubMed, Medscape, Scopus and Web of Science databases, etc.; as well as on the websites of international medical organizations using specific keywords: artificial intelligence, the use of artificial intelligence in obstetrics and gynecology, natural language processing models, artificial intelligence systems, ChatGPT, and telemedicine in obstetrics and gynecology. The search covered a period of 10 years.
Results. The effectiveness of artificial intelligence based on natural language processing models in various areas of obstetrics and gynecology has been proven. The possibilities of artificial intelligence for perinatal medicine and gynecological oncology have been identified, as well as in the field of cardiotocographic analysis based on artificial intelligence for intrapartum fetal monitoring in the direction of detecting premature births.
The effectiveness of artificial intelligence in the field of reproductive therapy (for predicting pregnancy after in vitro fertilization and identifying the most viable oocytes and embryos with a high probability of pregnancy) has been proven.
The effectiveness of artificial intelligence for assessing prognoses in patients with ovarian cancer has been confirmed (the ability to predict fetal survival with an accuracy of up to 97% has been proven) and for predicting the effectiveness of a particular type of therapy depending on the diagnosis. The capabilities of artificial intelligence for creating models capable of diagnosing early ovarian cancer and for analyzing precancerous images of the cervix have been identified.
In addition, it has been shown that in obstetric surgery, the use of physical artificial intelligence is more effective than virtual intelligence (it helps to reduce the duration of the operation and increase accuracy, which leads to a decrease in the number of surgical problems and complications).
At the same time, the effectiveness of using artificial intelligence in telemedicine to monitor patients remotely, tracking and regulating their condition using wearable devices connected to artificial intelligence systems, has been determined.
Conclusion. Thus, the use of AI in medicine and specifically in obstetrics and gynecology has a number of proven important advantages. However, further study is needed to improve and mitigate existing risks in order to enable deeper integration into practical medical practice.

Keywords

artificial intelligence, natural language processing models, artificial intelligence systems, ChatGPT

Author Biographies

Елена Валерьевна Litvinova,

candidate of medical sciences, docent of the department of obstetrics and gynecology

Oksana Vladimirovna Noskova,

candidate of medical sciences, docent of the department of obstetrics and gynecology

Article Details

Information about financing and conflict of interests

The study had no sponsorship.
The authors declare that they have no apparent or potential conflicts of interest related to the publication of this article.

How to Cite

Litvinova Е. В., & Noskova, O. V. (2026). ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В АКУШЕРСТВЕ И ГИНЕКОЛОГИИ. Mother and Baby in Kuzbass, 2, 137-142. https://mednauki.ru/index.php/MD/article/view/1375

References

1. Li SW, Kemp MW, Logan SJS, Dimri PS, Singh N, Mattar CNZ, et al. National University of Singapore Obstetrics and Gynecology Artificial Intelligence (NUS OBGYN-AI) Collaborative Group. ChatGPT outscored human candidates in a virtual objective structured clinical examination in obstetrics and gynecology. Am J Obstet Gynecol. 2023; 229(2): 172.e1-172.e12. doi: 10.1016/j.ajog.2023.04.020

2. Chatterjee J, Dethlefs N. This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy. Patterns (N Y). 2023; 4(1): 100676. doi: 10.1016/j.patter.2022.100676

3. Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems. 2022; 35: 27730-27744. doi: 10.48550/arXiv.2203.02155

4. Stiennon N, Ouyang L, Wu J, Ziegler D, Lowe R, Voss C, et al. Learning to summarize with human feedback. Advances in neural information processing systems. 2020; 33: 3008-3021. doi: 10.48550/arXiv.2009.01325

5. Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl. 2023; 82(3): 3713-3744. doi: 10.1007/s11042-022-13428-4

6. Landolsi MY, Hlaoua L, Ben Romdhane L. Information extraction from electronic medical documents: state of the art and future research directions. Knowl Inf Syst. 2023;65(2):463-516. doi: 10.1007/s10115-022-01779-1

7. Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol. 2023; 89: 30-37. doi: 10.1016/j.semcancer.2023.01.006

8. Lareyre F, Behrendt CA, Chaudhuri A, Lee R, Carrier M, Adam C, et al. Applications of artificial intelligence for patients with peripheral artery disease. J Vasc Surg. 2023; 77(2): 650-658.e1. doi: 10.1016/j.jvs.2022.07.160

9. Sinonquel P, Schilirò A, Verstockt B, Vermeire S, Bisschops R. Evaluating the potential of artificial intelligence in ulcerative colitis. Expert Rev Gastroenterol Hepatol. 2023; 17(2): 145-153. doi: 10.1080/17474124.2023.2166928

10. Loch AA, Lopes-Rocha AC, Ara A, Gondim JM, Cecchi GA, Corcoran CM, et al. Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders. JMIR Ment Health. 2022; 9(11): e41014. doi: 10.2196/41014

11. Huh S. Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study. J Educ Eval Health Prof. 2023; 20: 1. doi: 10.3352/jeehp.2023.20.1

12. Ramesh D, Sanampudi SK. An automated essay scoring systems: a systematic literature review. Artif Intell Rev. 2022; 55(3): 2495-2527. doi: 10.1007/s10462-021-10068-2

13. Stokel-Walker C. AI bot ChatGPT writes smart essays - should professors worry? Nature. 2022. doi: 10.1038/d41586-022-04397-7

14. Brown T, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language models are few-shot learners. Advances in neural information processing systems. 2020; 33: 1877-1901. doi: 10.48550/arXiv.2005.14165

15. Seval MM, Varlı B. Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front Med (Lausanne). 2023; 10: 1098205. doi: 10.3389/fmed.2023.1098205

16. Eriksson LSE, Epstein E, Testa AC, Fischerova D, Valentin L, Sladkevicius P, et al. Ultrasound-based risk model for preoperative prediction of lymph-node metastases in women with endometrial cancer: model-development study. Ultrasound Obstet Gynecol. 2020; 56(3): 443-452. doi: 10.1002/uog.21950

17. Van den Noort F, van der Vaart CH, Grob ATM, van de Waarsenburg MK, Slump CH, van Stralen M. Deep learning enables automatic quantitative assessment of puborectalis muscle and urogenital hiatus in plane of minimal hiatal dimensions. Ultrasound Obstet Gynecol. 2019; 54(2): 270-275. doi: 10.1002/uog.20181

18. Bahado-Singh RO, Sonek J, McKenna D, Cool D, Aydas B, Turkoglu O, et al. Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix. Ultrasound Obstet Gynecol. 2019; 54(1): 110-118. doi: 10.1002/uog.20168

19. Tsur A, Batsry L, Toussia-Cohen S, Rosenstein MG, Barak O, Brezinov Y, et al. Development and validation of a machine-learning model for prediction of shoulder dystocia. Ultrasound Obstet Gynecol. 2020; 56(4): 588-596. doi: 10.1002/uog.21878

20. Xie HN, Wang N, He M, Zhang LH, Cai HM, Xian JB, et al. Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound Obstet Gynecol. 2020; 56(4): 579-587. doi: 10.1002/uog.21967

21. Brocklehurst P; INFANT Collaborative Group. A study of an intelligent system to support decision making in the management of labour using the cardiotocograph - the INFANT study protocol. BMC Pregnancy Childbirth. 2016; 16: 10. doi: 10.1186/s12884-015-0780-0

22. Guh RS, Tsung-Chieh JW, Shao-Ping W. Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Syst Appl. 2011; 38: 4437-4449. doi: 10.1016/j.eswa.2010.09.112

23. Manna C, Nanni L, Lumini A, Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reprod Biomed Online. 2013; 26(1): 42-49. doi: 10.1016/j.rbmo.2012.09.015

24. Enshaei A, Robson CN, Edmondson RJ. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer. Ann Surg Oncol. 2015; 22(12): 3970-3975. doi: 10.1245/s10434-015-4475-6

25. Kann BH, Thompson R, Thomas CR Jr, Dicker A, Aneja S. Artificial Intelligence in Oncology: Current Applications and Future Directions. Oncology (Williston Park). 2019; 33(2): 46-53.

26. Hu L, Bell D, Antani S, Xue Z, Yu K, Horning MP, et al. An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening. J Natl Cancer Inst. 2019; 111(9): 923-932. doi: 10.1093/jnci/djy225

27. Moawad G, Tyan P, Louie M. Artificial intelligence and augmented reality in gynecology. Curr Opin Obstet Gynecol. 2019;31(5):345-348. doi: 10.1097/GCO.0000000000000559

28. Ajao MO, Clark NV, Kelil T, Cohen SL, Einarsson JI. Case Report: Three-Dimensional Printed Model for Deep Infiltrating Endometriosis. J Minim Invasive Gynecol. 2017; 24(7): 1239-1242. doi: 10.1016/j.jmig.2017.06.006

29. Dirie NI, Wang Q, Wang S. Two-Dimensional Versus Three-Dimensional Laparoscopic Systems in Urology: A Systematic Review and Meta-Analysis. J Endourol. 2018; 32(9): 781-790. doi: 10.1089/end.2018.0411

30. Song E, Yu F, Liu H, Cheng N, Li Y, Jin L, Hung CC. A Novel Endoscope System for Position Detection and Depth Estimation of the Ureter. J Med Syst. 2016; 40(12): 266. doi: 10.1007/s10916-016-0607-1

31. Onal S, Lai-Yuen S, Bao P, Weitzenfeld A, Greene K, Kedar R, Hart S. Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI. Int Urogynecol J. 2014; 25(6): 767-773. doi: 10.1007/s00192-013-2287-4

32. Nekooeimehr I, Lai-Yuen S, Bao P, Weitzenfeld A, Hart S. Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI. J Med Imaging (Bellingham). 2018; 5(1): 014008. doi: 10.1117/1.JMI.5.1.014008

33. Grünebaum A, Chervenak J, Pollet SL, Katz A, Chervenak FA. The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstet Gynecol. 2023; 228(6): 696-705. doi: 10.1016/j.ajog.2023.03.009

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