About
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
ISSN 0300-9092 (Print)
ISSN 2412-5679 (Online)
About
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
Logout
Obstetrics and Gynegology2026№4

Artificial intelligence and ovarian stimulation: improving the effectiveness and standardization of in vitro fertilization procedures

DOI
https://dx.doi.org/10.18565/aig.2026.70

Lokshin V.N., Azimbek A.M., Karibaeva Sh.K.

PERSONA International Clinical Center for Reproductology, Almaty, Republic of Kazakhstan

Background: One of the most challenging issues in the programs of in vitro fertilization (IVF) remains the selection of the optimal controlled ovarian stimulation (COS) protocol. The decision on the choice of the ovulation stimulation regimen is often based on the doctor’s experience, the availability of medications, and existing stimulation regimens. To minimize the impact of subjective factors on COS results, artificial intelligence (AI) and deep machine learning have become feasible in IVF programs in the past 3–4 years.
Objective: To analyze the current literature on the impact of AI technologies on the personalization of ovulation stimulation protocols in IVF programs.
Materials and methods: The search of scientific publications was conducted in the following databases: PubMed, Scopus, Wiley, Medline, Google Scholar, Cochrane, and Web of Science, covering a 10-year period. No restrictions were imposed on the study design.
Results: The analysis of the current literature showed that the integration of AI into reproductive medicine offers significant opportunities for improving the effectiveness and personalization of treatment, but it also presents a number of challenges. Key aspects include ensuring data quality and standardization, validating models on different patient groups, increasing trust among doctors and providing them with training, as well as addressing ethical and regulatory issues.
Conclusion: The integration of AI into the IVF process has the real potential to significantly transform IVF practice by reducing the influence of subjective factors on the treatment process. Due to its objectivity, personalization, accuracy, and efficiency, AI has the potential to significantly improve treatment outcomes. However, to fully realize the potential of AI, it is necessary to address challenges related to data quality, diversity, regulation, and the training of healthcare professionals.

Authors’ contributions: Lokshin V.N., Karibaeva Sh.K. – developing the concept and design of the study; Azimbek A.M. – conducting the scientific research, writing the text of the article; Lokshin V.N., Karibaeva Sh.K., Azimbek A.M. – interpretation of the scientific research results.
Conflicts of interest: The authors declare that there are no conflicts of interest.
Funding: The study was conducted without sponsorship.
For citation: Lokshin V.N., Azimbek A.M., Karibaeva Sh.K. Artificial intelligence and ovarian stimulation:
improving the effectiveness and standardization of in vitro fertilization procedures.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2026; (4): 59-66 (in Russian)
https://dx.doi.org/10.18565/aig.2026.70

Keywords

artificial intelligence (AI)
machine learning
assisted reproductive technologies (ART)
in vitro fertilization (IVF)
stimulation regimens
ovulation trigger
sperm
oocyte
embryo
Full text (in Russian)

References

  1. Fishel S. First in vitro fertilization baby – this is how it happened. Fertil. Steril. 2018; 110(1): 5-11. https://dx.doi.org/10.1016/j.fertnstert.2018.03.008
  2. Graham M.E., Jelin A., Hoon AH.Jr., Wilms Floet A.M., Levey E., Graham E.M. Assisted reproductive technology: short- and long-term outcomes. Dev. Med. Child Neurol. 2023; 65(1): 38-49. https://dx.doi.org/10.1111/dmcn.15332
  3. Алиева Ш., Локшин В., Цигенгагель О. Клинические перспективы применения искусственного интеллекта в урогинекологии: обзор литературы. Репродуктивная медицина (Центральная Азия). 2025; 1: 76-84. [Alieva Sh.U., Lokshin V.N., Tsigenhagel O.P. Clinical prospects for using artificial intelligence in urogynecology: a literature review. Reproductive Medicine (Central Asia). 2025; 1: 76-84 (in Russian)]. https://dx.doi.org/10.37800/RM.1.2025.465
  4. Soori M., Arezoo B., Dastres R. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics. 2023; 3: 54-70. https://dx.doi.org/10.1016/j.cogr.2023.04.001
  5. Wang R., Pan W., Jin L., Li Y., Geng Y., Gao C. et al. Artificial intelligence in reproductive medicine. Reproduction. 2019; 158(4): R139-54. https://dx.doi.org/10.1530/REP-18-0523
  6. Бегимбаева А., Рыбина А., Нигметова К., Сайлау Ж., Ермекова А., Карибаева Ш., Локшин В. Искусственный интеллект – ключ к развитию эмбриологической лаборатории. Репродуктивная медицина (Центральная Азия). 2024; 3: 42-9. [Begimbaeva А., Rybina А., Nigmetova К., Saylau Zh., Ermekova А., Karibayeva Sh., Lokshin V. Artificial intelligence – a key to the development of embryology laboratory. Reproductive Medicine (Central Asia). 2024; 3: 42-49 (in Russian)]. https://dx.doi.org/10.37800/RM.3.2024.42-49
  7. Siristatidis C., Vogiatzi P., Pouliakis A., Trivella M., Papantoniou N., Bettocchi S. Predicting IVF outcome: a proposed web-based system using artificial intelligence. In Vivo. 2016; 30(4): 507-12.
  8. Jiang V.S., Bormann C.L. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil. Steril. 2023; 120(1): 17-23. https://dx.doi.org/10.1016/j.fertnstert.2023.05.149
  9. Fernandez E.I., Ferreira A.S., Cecílio M.H.M., Chéles D.S., de Souza R.C.M., Nogueira M.F.G. et al. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J. Assist. Reprod. Genet. 2020; 37(10): 2359-76. https://dx.doi.org/10.1007/s10815-020-01881-9
  10. Olawade D.B., Teke J., Adeleye K.K., Weerasinghe K., Maidoki M., Clement David-Olawade A. Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments. J. Gynecol. Obstet. Hum. Reprod. 2025; 54(3): 102903. https://dx.doi.org/10.1016/j.jogoh.2024.102903
  11. Pavlovic Z.J., Jiang V.S., Hariton E. Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey. Curr. Opin. Obstet. Gynecol. 2024; 36(4): 211-7. https://dx.doi.org/10.1097/GCO.0000000000000951
  12. Hanassab S., Abbara A., Yeung A.C., Voliotis M., Tsaneva-Atanasova K., Kelsey T.W. et al. The prospect of artificial intelligence to personalize assisted reproductive technology. NP J. Digit. Med. 2024; 7(1): 55. https://dx.doi.org/10.1038/s41746-024-01006-x
  13. Шайлиева Ш.Л., Мамчуева Д.Х., Вишневская А.П., Джалаева Х.Ш., Рамазанова Э.Г., Кокаева Я.Р., Елоева З.М., Айсанова Д.Р., Виноградова А.С., Туко Р.Р., Синева А.В., Валиуллина Л.А., Куцева А.А. Возможности применения искусственного интеллекта в современной гинекологии. Акушерство, гинекология и репродукция. 2024; 18(4): 563-80. [Shailieva Sh.L., Mamchueva D.Kh., Vishnevskaya A.P., Dzhalaeva Kh.Sh., Ramazanova E.G., Kokaeva Y.R., Eloeva Z.M., Aisanova D.R., Vinogradova A.S., Tuko R.R., Sineva A.V., Valiullina L.A., Kutseva A.A. An opportunity for using artificial intelligence in modern gynecology. Obstetrics, Gynecology and Reproduction. 2024; 18(4): 563-80 (in Russian)]. https://dx.doi.org/10.17749/2313-7347/ob.gyn.rep.2024.511
  14. AlSaad R., Abd-Alrazaq A., Choucair F., Ahmed A., Aziz S., Sheikh J. Harnessing artificial intelligence to predict ovarian stimulation outcomes in in vitro fertilization: scoping review. J. Med. Internet Res. 2024; 26: e53396. https://dx.doi.org/10.2196/53396
  15. Asada Y., Shinohara T., Yonezawa S., Kinugawa T., Asano E., Kojima M. et al. Development of an AI-based support system for controlled ovarian stimulation. Reprod. Med. Biol. 2024; 23(1): e12603. https://dx.doi.org/10.1002/rmb2.12603
  16. Dabbagh Rezaeiyeh R., Mehrara A., Mohammad Ali Pour A., Fallahi J., Forouhari S. Impact of various parameters as predictors of the success rate of in vitro fertilization. Int. J. Fertil. Steril. 2022; 16(2): 76-84. https://dx.doi.org/10.22074/IJFS.2021.531672.1134
  17. Martínez L.C., Murria L., Angeles Valera M., Cobo A., Meseguer M. Testing the ability of an artificial intelligence (AI) algorithm in predicting implantation and ongoing pregnancy potential of vitrified-warmed blastocysts from a single image. Fertil. Steril. 2023; 120(4): e41-2. https://dx.doi.org/10.1016/j.fertnstert.2023.08.148
  18. Ozturk S. Selection of competent oocytes by morphological criteria for assisted reproductive technologies. Mol. Reprod. Dev. 2020; 87(10): 1021-36. https://dx.doi.org/10.1002/mrd.23420
  19. Cherouveim P., Velmahos C., Bormann C.L. Artificial intelligence for sperm selection – a systematic review. Fertil. Steril. 2023; 120(1): 24-31. https://dx.doi.org/10.1016/j.fertnstert.2023.05.157
  20. You J.B., McCallum C., Wang Y., Riordon J., Nosrati R., Sinton D. Machine learning for sperm selection. Nat. Rev. Urol. 2021; 18: 387-403. https://dx.doi.org/10.1038/s41585-021-00465-1
  21. Celebi D., Omur A.D., Akarsu S.A. Artificial intelligence in gamete cell selection and semen microbiologic analysis. J. Clin. Vet. Res. 2022; 2(2): 1-7. https://dx.doi.org/10.54289/JCVR2200107
  22. Farías A.F.S., Sakkas D., Chavez Badiola A., Ocali O., Mendizabal G., Valencia R. et al. Single-sperm motility analysis during ICSI using an artificial intelligence sperm identification software (SID) and correlation with morphology. Fertil. Steril. 2022; 118(4): e56-7. https://dx.doi.org/10.1016/j.fertnstert.2022.08.178
  23. Shahali S., Murshed M., Spencer L., Tunc O., Pisarevski L., Conceicao J. et al. Morphology classification of live unstained human sperm using ensemble deep learning. Adv. Intell. Syst. 2024; 6(11): 2400141. https://dx.doi.org/10.1002/aisy.202400141
  24. Letterie G., MacDonald A., Shi Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod. Biomed. Online. 2022; 44(2): 254-60. https://dx.doi.org/10.1016/j.rbmo.2021.10.006
  25. Si K., Huang B., Jin L. Application of artificial intelligence in gametes and embryos selection. Hum Fertil (Camb). 2023; 26(4): 757-77. https://dx.doi.org/10.1080/14647273.2023.2256980
  26. Salih M., Austin C., Warty R.R., Tiktin C., Rolnik D.L., Momeni M. et al. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum. Reprod. Open. 2023; 2023(3): hoad031. https://dx.doi.org/10.1093/hropen/hoad031
  27. Barnes J., Brendel M., Gao V.R., Rajendran S., Kim J., Li Q. et al. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit. Health. 2023; 5(1): e28-e40. https://dx.doi.org/10.1016/S2589-7500(22)00213-8
  28. Letterie G., Mac Donald A. Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil. Steril. 2020; 114(5): 1026-31. https://dx.doi.org/10.1016/j.fertnstert.2020.06.006
  29. Correa N., Cerquides J., Arcos J.L., Vassena R. Supporting first FSH dosage for ovarian stimulation with machine learning. Reprod. Biomed. Online. 2022; 45(5): 1039-54. https://dx.doi.org/10.1016/j.rbmo.2022.06.010
  30. Fanton M., Nutting V., Rothman A., Maeder-York P., Hariton E., Barash O. et al. An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation. Reprod. Biomed. Online. 2022; 45(6): 1152-9. https://dx.doi.org/10.1016/j.rbmo.2022.07.010
  31. Canon C., Leibner L., Fanton M., Chang Z., Suraj V., Lee J.A. et al. Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center, prospective study. Sci. Rep. 2024; 14(1): 18721. https://dx.doi.org/10.1038/s41598-024-69165-1
  32. Asada Y., Shinohara T., Yonezawa S., Kinugawa T., Asano E., Kojima M. et al. Development of an AI-based support system for controlled ovarian stimulation. Reprod. Med. Biol. 2024; 23(1): e12603. https://dx.doi.org/10.1002/rmb2.12603
  33. Hariton E., Chi E.A., Chi G., Morris J.R., Braatz J., Rajpurkar P. et al. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil. Steril. 2021; 116(5): 1227-35. https://dx.doi.org/10.1016/j.fertnstert.2021.06.018
  34. Akhil G., Jose B., Ernesto B., Jose A.R., Antonio P., Marcos M. Machine learning tool for predicting mature oocyte yield and trigger day from start of stimulation: towards personalized treatment. Reprod. Biomed. Online. 2025; 50(2): 104441. https://dx.doi.org/10.1016/j.rbmo.2024.104441
  35. Ferrand T., Boulant J., He C., Chambost J., Jacques C., Pena C.A. et al. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum. Reprod. 2023; 38(10): 1918-26. https://dx.doi.org/10.1093/humrep/dead163
  36. Nasatzky M., Belicha Y., Fainaru O. Clinical parameters that predict a premature LH rise in patients undergoing ovarian stimulation for IVF. Gynecol. Endocrinol. 2024; 40(1): 2365913. https://dx.doi.org/10.1080/09513590.2024.2365913
  37. Banerjee P., Choi B., Shahine L.K., Jun S.H., O'Leary K., Lathi R.B. et al. Deep phenotyping to predict live birth outcomes in in vitro fertilization. Proc. Natl. Acad. Sci. USA. 2010; 107(31): 13570-5. https://dx.doi.org/10.1073/pnas.1002296107
  38. Pacheco F., Arrach N. Artificial intelligence in ovarian stimulation. Reprod. Biomed. Online. 2024; 49(Suppl.): 104513. https://dx.doi.org/10.1016/j.rbmo.2024.104513
  39. Zegers-Hochschild F., Adamson G.D., Dyer S., Racowsky C., de Mouzon J., Sokol R. et al. The International glossary on infertility and fertility care, 2017. Fertil. Steril. 2017; 108(3): 393-406. https://dx.doi.org/10.1016/j.fertnstert.2017.06.005
  40. World Health Organization. Infertility prevalence estimates, 1990–2021. 3 April 2023. Available at: https://www.who.int/publications/i/item/978920068315

Received 27.02.2026

Accepted 09.04.2026

About the Authors

Vyacheslav N. Lokshin, Dr. Med. Sci., Professor, Academician of the National Academy of Sciences of the Republic of Kazakhstan, President of the Kazakhstan Association of Reproductive Medicine, President of the International Academy of Reproductology, Director of the PERSONA International Clinical Center for Reproductology, Almaty, Kazakhstan, +7(701)755-82-09, v_lokshin@persona-ivf.kz, https://orcid.org/0000-0002-4792-5380
Aisana M. Azimbek, obstetrician-gynecologist, PERSONA International Clinical Center for Reproductology, Almaty, Kazakhstan, +7(775)279-67-76,
aisana.sutaliyeva@gmail.com, https://orcid.org/0000-0002-3038-4223
Sholpan K. Karibaeva, PhD, Kazakh National Medical University named after S.D. Asfendiyarov; obstetrician-gynecologist of the highest category, PERSONA International Clinical Center for Reproductology, Almaty, Kazakhstan, +7(701)755-06-75, sh.karibaeva@gmail.com, https://orcid.org/0000-0001-5691-8652
Corresponding author: Aisana M. Azimbek, aisana.sutaliyeva@gmail.com

Similar Articles

№3 / 2025
Yasnaya D.V., Likhacheva V.V., Proskuryakova L.A., Alontseva V.V., Azarova O.V.
Psychoemotional state of women undergoing in vitro fertilization treatment
№11 / 2025
Amyan T.S., Patskova P.O., Kurushin N.D., Timofeeva A.V., Gavisova A.A.
Epigenetic regulation of reproductive potential: from folliculogenesis to implantation
№12 / 2024
Ob’edkova K.V., Ryzhov Ju.R., Khalenko V.V., Ishchuk M.A., Sagurova Ya.M., Lesik E.A., Komarova E.M., Gzgzyan A.M., Bespalova O.N., Kogan I.Yu., Tapilskaya N.I.
Mitochondrial dysfunction and embryo quality: prospects for antioxidant correction containing myo-inositol and D-chiroinositol in a 5:1 ratio, manganese and folic acid
№2 / 2025
Drapkina Yu.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A.
Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies
№12 / 2024
Iurova M.V., Tokareva A.O., Chagovets V.V., Starodubtseva N.L., Frankevich V.E.
Differential diagnosis of early-stage ovarian cancer based on the bioinformatic analysis of the blood metabolome
By continuing to use our site, you consent to the processing of cookies that ensure the proper functioning of the site.