Let’s Talk Fertility: Recent Technological Advances in Clinical Practice
DOI:
https://doi.org/10.33178/SMJ.2026.3Keywords:
Infertility, Assisted reproductive technologies, Artificial intelligence, Preimplantation genetic testing, TelemedicineAbstract
Infertility affects approximately one in six couples worldwide and continues to be a growing clinical and social challenge. Assisted reproductive technologies (ART), namely in-vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), have facilitated tremendous advances in fertility care, but success rates remain low, and patients continue to face challenges including cost and access to services. Emerging technological advancements in artificial intelligence (AI), preimplantation genetic testing (PGT), and telemedicine are transforming fertility care with the potential to overcome many of these barriers. AI integrates individualization with technology to personalize strategies for fertilization and clinical decision making to improve safety and success in each IVF cycle. Advances in PGT, including next generation sequencing (NGS) and non-invasive pregenetic testing (niPGT) show promise in screening and detection of genetic abnormalities which contribute to optimizing reproductive outcomes. Telemedicine allows for expansion of access to fertility care beyond traditional settings, helping to overcome barriers including geographical distance and increasing access to fertility health services. Collectively, these advances are shifting fertility care towards individualized, precise, and accessible care, with the potential for global impacts on reproductive outcomes.
References
1. World Health Organization. Infertility [Internet]. Geneva: WHO; 2025 Nov 28 [cited 2026 Jan 14]. Available from: https://www.who.int/news-room/fact-sheets/detail/infertility.
2. Timoney A. Data: Assisted Human Reproduction [Internet]. Dublin: Houses of the Oireachtas Library & Research Service (L&RS) 2022 Feb. Available from: https://data.oireachtas.ie/ie/oireachtas/libraryResearch/2022/2022-02-28_l-rs-note-assisted-human-reproduction-data_en.pdf.
3. Olawade DB, Teke J, Adeleye KK, Weerasinghe K, Maidoki M, David-Olawade AC. Artificial intelligence in in-vitro fertilization (IVF): a new era of precision and personalization in fertility treatments. J Gynecol Obstet Hum Reprod. 2025 Mar;54(3):102903. doi:10.1016/j.jogoh.2024.102903. PMID: 39733809.
4. Adamson GD, Zegers-Hochschild F, Dyer S. Global fertility care with assisted reproductive technology. Fertil Steril. 2023 Sep;120(3 Pt 1):473–82. doi:10.1016/j.fertnstert.2023.01.013. PMID: 36642305.
5. Kaveh S, Ghaffari A, Sohrabei S. Investigating artificial intelligence in predicting and evaluating sperm and embryo quality in in vitro fertilization (IVF): a systematic review. Discov Artif Intell. 2025 Jul;5(1):183. doi:10.1007/s44163-025-00420-8.
6. Sun L, Li J, Zeng S, Luo Q, Miao H, Liang Y, et al. Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos. Chin Med J (Engl). 2024 Aug;137(16):1939–49. doi:10.1097/CM9.0000000000003162. PMID: 38997251.
7. Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, et al. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med. 2024 Nov;30(11):3114–20. doi:10.1038/s41591-024-03166-5. PMID: 39122964.
8. Cherouveim P, Velmahos C, Bormann CL. Artificial intelligence for sperm selection—a systematic review. Fertil Steril. 2023 Jul 1;120(1):24–31. doi:10.1016/j.fertnstert.2023.05.157. PMID: 37236418.
9. Carrión-Sisternas L, Viloria T, Martin E, Carrión T, Remohí J, Meseguer M. Automated AI for real-time sperm selection in ICSI: reducing variability and studying the role of sperm in embryo development. Reprod Biol Endocrinol. 2025 Nov;23(1):155. doi:10.1186/s12958-025-01479-9. PMID: 41299699.
10. Canon C, Leibner L, Fanton M, Chang Z, Suraj V, Lee JA, et al. Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions: a multicenter prospective study. Sci Rep. 2024 Dec;14(1):18721. doi:10.1038/s41598-024-69165-1. PMID: 39164339.
11. Bixby CJ, Miller B. Real-world use of an artificial intelligence–powered clinical decision support tool for ovarian stimulation. F S Rep. 2025 Jun;6(2):140. doi:10.1016/j.xfre.2025.01.015. PMID: 40620368.
12. Mrugacz G, Mospinek A, Jagielska M, Miszczak D, Matosek A, Ducher-Hanaka M, et al. Artificial intelligence in routine IVF practice. Biology (Basel). 2025 Dec;15(1):42. doi:10.3390/biology15010042. PMID: 41514884.
13. Parikh FR, Athalye AS, Naik NJ, Naik DJ, Sanap RR, Madon PF. Preimplantation genetic testing: its evolution, where are we today? J Hum Reprod Sci. 2018 Oct;11(4):306–14. doi:10.4103/jhrs.jhrs_132_18. PMID: 30787513.
14. Fernandes SLE, de Carvalho FAG. Preimplantation genetic testing: a narrative review. Porto Biomed J. 2024 Jul;9(4):262. doi:10.1097/j.pbj.0000000000000262. PMID: 38993950.
15. Fiorentino F, Biricik A, Bono S, Spizzichino L, Cotroneo E, Cottone G, et al. Development and validation of a next-generation sequencing-based protocol for 24-chromosome aneuploidy screening of embryos. Fertil Steril. 2014 May;101(5):1479-90. doi:10.1016/j.fertnstert.2014.01.051. PMID: 24613537.
16. Friedenthal J, Maxwell SM, Munné S, Kramer Y, McCulloh DH, McCaffrey C, et al. Next-generation sequencing for preimplantation genetic screening improves pregnancy outcomes compared with array comparative genomic hybridization in single thawed euploid embryo transfer cycles. Fertil Steril. 2018 Apr 1;109(4):627–32. doi:10.1016/j.fertnstert.2017.12.017. PMID: 29605407.
17. Rubio C, Bellver J, Rodrigo L, Castillón G, Guillén A, Vidal C, et al. In vitro fertilization with preimplantation genetic diagnosis for aneuploidies in advanced maternal age: a randomized controlled study. Fertil Steril. 2017 May 1;107(5):1122–9. doi:10.1016/j.fertnstert.2017.03.011. PMID: 28433371.
18. Tong J, Niu Y, Wan A, Zhang T. Next-generation sequencing-based preimplantation genetic testing for aneuploidy (PGT-A) of trophectoderm biopsy for recurrent implantation failure patients: a retrospective study. Reprod Sci. 2021 Jul 1;28(7):1923–9. doi:10.1007/s43032-021-00519-0. PMID: 33709375.
19. Pei Z, Deng K, Lei C, Du D, Yu G, Sun X, et al. Identifying balanced chromosomal translocations in human embryos by Oxford nanopore sequencing and breakpoint region analysis. Front Genet. 2022 Jan;12. doi:10.3389/fgene.2021.810900.
20. Voros C, Darlas M, Athanasiou D, Athanasiou A, Athanasiou A, Bananis K, et al. Evaluation of the effectiveness and accuracy of non-invasive preimplantation genetic testing (niPGT) compared to invasive embryo biopsy. Biomedicines. 2025 Aug;13(8):2010. doi:10.3390/biomedicines13082010. PMID: 40868262.
21. Kuznyetsov V, Madjunkova S, Antes R, Abramov R, Motamedi G, Ibarrientos Z, et al. Evaluation of a novel non-invasive preimplantation genetic screening approach. PLoS One. 2018 May;13(5):e0197262. doi:10.1371/journal.pone.0197262. PMID: 29746572.
22. Valdera Simbrón CJ, Hernández Rodríguez C. Telemedicine in assisted reproduction. Reprod Biomed Online. 2021 Jan 1;42(1):269–72. doi:10.1016/j.rbmo.2020.11.016. PMID: 33293221.
23. Alexander VM, Schelble AP, Omurtag KR. Traits of patients seen via telemedicine versus in person for new-patient visits in a fertility practice. F S Rep. 2021 Jun 1;2(2):224–9. doi:10.1016/j.xfre.2021.04.001.
24. Hernández C, Valdera CJ, Cordero J, López E, Plaza J, Albi M. Impact of telemedicine on assisted reproduction treatment in the public health system. J Healthc Qual Res. 2020 Jan;35(1):27–34. doi:10.1016/j.jhqr.2019.08.004. PMID: 31883955.
25. Tran HP, Nguyen NN, Ho NT, Tran TTT, Ly LT, Hoang TTD, et al. The impacts of telemedicine on assisted reproduction: a systematic review and meta-analysis. Reprod Biomed Online. 2024 May 1;48(5). doi:10.1016/j.rbmo.2023.103752. PMID: 38489925.
26. Imrie R, Ghosh S, Narvekar N, Vigneswaran K, Wang Y, Savvas M. Socioeconomic status and fertility treatment outcomes in high-income countries: a review of the current literature. Hum Fertil (Camb). 2023;26(1):27–37. doi:10.1080/14647273.2021.1957503. PMID: 34315303.
27. Kelley AS, Qin Y, Marsh EE, Dupree JM. Disparities in accessing infertility care in the United States: results from the National Health and Nutrition Examination Survey, 2013–16. Fertil Steril. 2019 Sep 1;112(3):562–8. doi:10.1016/j.fertnstert.2019.04.044. PMID: 31262522.
28. Gogia SB, Maeder A, Mars M, Hartvigsen G, Basu A, Abbott P. Unintended consequences of telehealth and their possible solutions: contribution of the IMIA Working Group on Telehealth. Year Med Inform. 2016 Nov;(1):41–6. doi:10.15265/IY-2016-012. PMID: 27830229.
29. Mirzaeian L, Eivazkhani F, Saber M, Moini A, Esfandiari F, Valojerdi MR, et al. In vivo oogenesis of oogonial and mesenchymal stem cells seeded in transplanted ovarian extracellular matrix. J Ovarian Res. 2023 Mar 20;16(1):56. doi:10.1186/s13048-023-01131-3. PMID: 36941728.
30. Romualdez-Tan MV. Modelling in vitro gametogenesis using induced pluripotent stem cells: a review. Cell Regen. 2023 Oct 16;12(1):33. doi:10.1186/s13619-023-00176-5.
Published
License
Copyright (c) 2026 Alyssa Belfiore, Magdalena Princ, Vivienne Wang, Lauren Birchenough, Emma Wilson, Hio Weng Chu, Nimisha Elavarathan, Azusa Murphy, Elizabeth Sentner, Nicole Crellin-Parsons
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
