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Marziyat Sarbasheva et al. RT&A, Special Issue № 6 (81), Part-3, INNOVATIVE TECNOLOGIES AND ARTIFICIAL..._Volume 19, December 2024
INNOVATIVE TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE IN REPRODUCTIVE MEDICINE
Marziyat Sarbasheva, Islam Eleskhanov Magomed-Salah Deshiev, Deniel Bekhoev, Hasan Yandiev
Medical Academy named after Kh.M. Berbekov, RUSSIA
Abstract
Innovative technologies in reproductive medicine are radically changing approaches to the treatment of infertility, genetic diseases and reproductive health problems. Modern methods such as assisted reproductive technologies (ART), including in vitro fertilization (IVF) and embryo cryopreservation, have significantly increased the chances of successful conception. Key achievements have also included preimplantation genetic testing (PGT), which allows identifying genetic abnormalities in embryos before they are transferred to the uterus, and genome editing technologies (such as CRISPR), which offer the prospect of correcting genetic defects at an early stage of development. In addition, stem cell research is actively developing, which in the future may make it possible to create artificial gametes, which will be a solution for people who lack functional germ cells. Another significant breakthrough is uterine transplantation, which gives a chance to bear a child to women who do not have their own functional uterus. These advances not only increase the success of reproductive procedures, but also open up new horizons in the field of personalized medicine and raise important ethical questions related to reproductive technologies. Innovative developments in reproductive medicine are aimed at improving the quality of life of people and expanding opportunities to overcome barriers to parenthood, making them an important component of modern healthcare.
Keywords: innovative technologies, reproductive medicine, ART, IVF, cryopreservation, preimplantation genetic testing
I. Introduction
Innovative technologies in reproductive medicine are revolutionizing the way we approach fertility, genetic disorders, and overall reproductive health. Over the past few decades, significant advancements have emerged, offering new hope to individuals and couples struggling with infertility and hereditary diseases. These developments are particularly evident in the realm of assisted reproductive technologies (ART), such as in vitro fertilization (IVF), which has dramatically improved success rates for conception. The ability to freeze embryos and eggs through cryopreservation has further expanded reproductive options, allowing patients to preserve their fertility for future use.
In parallel, preimplantation genetic testing (PGT) now enables the screening of embryos for genetic abnormalities before implantation, reducing the risk of passing on hereditary diseases and improving the overall chances of a healthy pregnancy. Additionally, cutting-edge gene-editing technologies like CRISPR are opening new doors by offering the potential to correct genetic defects at the embryonic stage, presenting possibilities for eliminating certain inherited disorders altogether.
Research into stem cell therapy and the potential to develop artificial gametes (sperm and eggs) is another exciting frontier, especially for individuals who cannot produce viable
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reproductive cells. This could provide a breakthrough for those previously considered untreatable by conventional methods. Furthermore, the development of uterine transplantation offers women who lack a functional uterus the opportunity to carry a pregnancy to term, which was previously impossible.
These innovative technologies are not only enhancing the clinical success of fertility treatments but are also driving a deeper understanding of human reproduction at the molecular and genetic levels. This progress is facilitating the rise of personalized reproductive medicine, where treatments can be tailored to an individual's specific genetic and biological profile.
As the field of reproductive medicine evolves, it brings with it significant ethical, legal, and social implications. Questions around the use of genetic editing, embryo selection, and reproductive autonomy raise complex debates about the future of human reproduction. At the same time, the promise of these technologies offers the potential to overcome previously insurmountable barriers to parenthood, fundamentally transforming the landscape of reproductive health.
In light of these rapid advancements, reproductive medicine is entering a new era, where science and technology intersect to provide more effective, individualized solutions for a growing number of people around the world.
II. Methods
The study and application of innovative technologies in reproductive medicine involve a multidisciplinary approach, incorporating advanced scientific techniques, medical procedures, and cutting-edge research. Key methods used in the field include:
-In Vitro Fertilization (IVF):
This method involves retrieving eggs from the ovaries and fertilizing them with sperm in a laboratory setting. After successful fertilization, the embryos are cultured and then transferred to the uterus for implantation. IVF is one of the most common assisted reproductive technologies used to treat a variety of infertility issues.
-Preimplantation Genetic Testing (PGT):
PGT is a technique used to screen embryos for genetic abnormalities before implantation during IVF cycles. There are different forms of PGT, such as PGT-A for detecting aneuploidy (chromosomal abnormalities) and PGT-M for identifying specific monogenic genetic disorders. This method increases the chances of a healthy pregnancy by selecting genetically viable embryos.
-Uterine Transplantation:
Uterine transplantation involves transplanting a healthy uterus into a woman who does not have a functional uterus, often due to congenital conditions or previous surgery. After the transplant, IVF is typically used to implant embryos into the uterus, allowing the woman to carry and deliver a child. This method is particularly groundbreaking for individuals who otherwise could not achieve pregnancy.
III. Results
Artificial intelligence (AI) is gradually being incorporated into various fields of medicine, including reproductive medicine. In this domain, AI can enhance the selection and prediction of sperm cells, eggs (oocytes), and embryos, as well as improve predictive models for in vitro fertilization (IVF). The use of AI in reproductive medicine is driven by the emotional and psychological toll experienced by individuals or couples who are unable to conceive. However, the application of AI in this field remains in the early experimental stages, raising complex ethical and normative issues. Ethical concerns arise due to the lack of robust evidence supporting the effectiveness of certain AI systems, as well as challenges in obtaining fully informed consent from
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affected individuals.
Additional ethical considerations include the potential risks to future offspring and the difficulty of ensuring that patients receive adequate information about the AI-driven processes. The ability to fulfill the desire for children has a significant impact on patient well-being and reproductive autonomy. More accurate predictions and the possibility of freeing up physicians to spend more time with their patients are positive outcomes. However, it is essential that clinicians handle patient data responsibly. Since multiple actors are involved in diagnosis and treatment decisions when AI is used, accountability becomes a concern, particularly in the case of errors.
Fairness issues also emerge regarding resource distribution and reimbursement for AI-based treatments. Therefore, before integrating AI into clinical practice, it is critical to thoroughly evaluate the quality and scope of the data being used and address transparency concerns. In the medium and long term, it is necessary to consider the potential negative impacts and societal changes that may arise from the widespread use of AI in reproductive medicine.
IV. Discussion
Artificial intelligence (AI) is advancing rapidly, with applications being integrated across various sectors, including several fields of medicine. Complex AI algorithms are capable of analyzing vast amounts of data to enhance diagnoses, prognoses, and preventive measures. In recent years, the analysis of image data has emerged as a promising application area, with some studies suggesting that AI-generated results may surpass those of human experts. Additionally, AI is expected to streamline workflow processes in hospitals and improve patient monitoring.
In reproductive medicine, new applications of AI methods are being explored. Individuals or couples desiring children but facing infertility often experience significant life crises and a diminished quality of life. The inability to conceive can be seen as a barrier to fulfilling the widely accepted need for reproduction and child-rearing, leading to psycho-existential concerns as it threatens individuals' visions for their future. The birth of Louise Brown over 40 years ago marked a pivotal moment, ushering in a new era of hope for infertile individuals and couples by making in vitro fertilization (IVF) a viable option. Estimates suggest that infertility affects up to 186 million people globally.
The ongoing advancement of reproductive technologies—such as oocyte and embryo cryopreservation, IVF, preimplantation genetic diagnostics (PGD), and the ability to select preimplantation embryos based on morphokinetic criteria—has significantly improved clinical pregnancy rates over the past four decades.
However, several challenges remain. In Germany, the average likelihood of achieving a live birth after a fresh embryo transfer through IVF or intracytoplasmic sperm injection (ICSI) is approximately 24%, while the probability for previously cryopreserved embryos is about 20%. Essentially, despite successful fertilization and cell division, only one in four fresh embryo transfers and one in five cryo-transfers will result in a live birth. The age of the woman's oocytes and the quality of the embryos are critical factors influencing the success of IVF treatments. Unfortunately, reliable methods for accurately assessing the quality of oocytes, sperm, and embryos are still lacking. Although preimplantation diagnostics can be employed before embryo transfer, this approach is ethically contentious. In Germany, it is only permitted under specific conditions, is technically complex, costly, and carries risks of injury or destruction to the embryo.
The impetus for developing AI technologies in reproductive medicine lies in the goal of enhancing treatment outcomes and prognoses for infertile patients by synthesizing large datasets to derive meaningful insights. This context shapes our ethical discussion, which considers both the needs of individuals with unfulfilled parental desires and the application of specific AI technologies to achieve medical objectives. The principles of healing, helping, and alleviating—
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aligned with the overarching principle of beneficence—provide the ethical framework for this dialogue. Practices such as extracorporeal fertilization and selective reproduction (e.g., following PGD) remain contentious, and research alongside the potential implementation of AI in clinical settings introduces complex normative questions regarding both content and procedures. Therefore, a proactive ethical debate is essential, even as AI research and its application in reproductive medicine are still in their infancy.
In light of this, we will begin with a brief overview of the current state of research and development in this field, followed by a structured ethical analysis of the potential uses of AI methods in reproductive medicine. We will then explore the impact of these technologies on the physician-patient relationship. Finally, we will consider possible future developments in AI-driven reproductive medicine and briefly address the associated social challenges.
In recent decades, advancements in reproductive medicine, such as preimplantation genetic diagnosis (PGD) and intracytoplasmic sperm injection (ICSI), have frequently transitioned from laboratory settings to clinical applications without undergoing thorough evaluations of their effectiveness and safety. Research involving human subjects in reproductive medicine is particularly sensitive from an ethical standpoint and presents unique challenges. Initially, the intended mother is the primary subject affected by the study, shouldering most of the physical and psychological burdens. However, the research and expected innovations in assisted reproduction, including AI applications, can result in the birth of offspring who are also impacted by the potential risks of these experiments, even though they cannot consent to participate. Additionally, the long-term effects on these offspring, resulting from experimental procedures in assisted reproduction, may not be fully understood for years. This situation is complicated by ethical questions surrounding the testing of new procedures involving human embryos.
In all future research concerning the use of AI in reproductive medicine, it will be crucial to provide well-considered information to intended mothers or parents. It's essential to recognize the unfavorable scenarios that can arise when conveying information to potential parents; participants in studies might develop false hopes or unrealistic expectations, especially when innovative, attention-grabbing technologies are being tested. This is particularly relevant for women or couples who have endured long-standing difficulties with childlessness, as they represent a vulnerable group. Individuals participating in such studies may feel they are facing their "last chance" to realize their parental aspirations, necessitating special precautions to ensure that participation is truly voluntary.
Patients' Well-Being and Autonomy
One of the primary objectives of incorporating AI systems into predictive models for IVF is to enhance outcomes compared to traditional reproductive medicine techniques. A key indicator of such improvements would be an increased baby-take-home rate, which reflects a higher likelihood that assisted fertilization will result in a live birth. This enhancement could alleviate both the psychological and physical suffering of patients. The normative principle of prioritizing patient welfare in assisted reproductive medicine involves delivering the most suitable treatment based on objectively measurable medical criteria and considering the patient's subjective experience, preferences, and satisfaction with the treatment. The future integration of AI technology could potentially benefit both aspects.
An analysis of 122,560 treatment cycles in Germany revealed that 45,699 patients discontinued therapy after having a child, while 76,861 (62.7%) stopped before achieving their desire to conceive. Various factors contributed to therapy discontinuation, such as a lack of transferable embryos due to immature oocytes, unsuccessful oocyte retrieval, failed fertilization attempts, and halted embryo development. These challenges can dissuade patients from continuing treatment. Other reasons for discontinuation included inadequate response to stimulation, overstimulation syndrome, premature ovulation, and incorrect hormone
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administration, which could lead to unsuccessful treatment outcomes. The emotional toll of not conceiving despite multiple reproductive medical interventions is believed to contribute significantly to therapy discontinuation. Studies have also highlighted relationship difficulties and other personal issues alongside the physical and psychological burdens of infertility treatment. Furthermore, societal stigmas related to infertility—such as shame and social exclusion—often exacerbate patients' experiences.
Enhanced and expedited treatment options, potentially offered through future AI applications, could help some couples mitigate these stresses, thereby contributing positively to their overall well-being.
Reproductive Autonomy
The use of AI in reproductive medicine can also be evaluated in terms of patients' reproductive autonomy. Reproductive autonomy is a normative concept defined as individuals' capacity to make informed, uncoerced decisions about their own reproductive choices. From this perspective, measures that empower patients to exercise their reproductive freedom, such as AI applications in reproductive medicine, should ideally be accessible to all individuals seeking to conceive. Conversely, restrictions on reproductive autonomy should only be permissible if new technologies in reproductive medicine demonstrably harm patients or their potential offspring. This autonomy can be viewed as an entitlement, as the ability to exercise control over reproduction is fundamental to personal identity. Therefore, efforts should be made to assist couples in achieving their parenting aspirations, which includes ensuring equitable access to emerging reproductive technologies, including AI applications. However, it is vital to avoid the risks associated with inadequate information, as highlighted earlier, to allow patients to fully exercise their reproductive autonomy in this experimental field.
Benefits and Challenges for the Physician-Patient Relationship
The potential integration of AI into reproductive medicine will undoubtedly influence a cornerstone of medical practice: the physician-patient relationship. AI may necessitate a reevaluation of the reproductive specialist's professional role, as they will serve as the intermediary between algorithmic outputs and treatment decisions. Physicians will need to address not only the biological factors contributing to infertility but also the psychosocial and emotional stresses patients face during treatment. The future utilization of AI could provide significant benefits, enabling better predictions of individual patients' likelihood of becoming pregnant. Existing data can inform these probability assessments, such as using the woman's age. A more precise prognosis could empower physicians to deliver better-informed recommendations and therapies, ultimately enhancing treatment efficiency by optimizing the selection of sperm, oocytes, and embryos. Even minimal contributions from potential AI applications could provide valuable support amid the stresses associated with infertility treatment. Additionally, it is anticipated that automated support systems in medicine will free up physicians to devote more time to building stronger relationships with their patients.
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