In-Depth Analysis of the Future of In-Vitro Fertilization: Artificial Intelligence, Genetic Engineering, and the Ethical Implications of so-called “Designer Babies”
In-vitro fertilization (IVF) has played a significant role in assisted reproductive technologies (such as surrogacy) since its inception in 1978. As science and technology advance, the integration of artificial intelligence (AI) and genetic engineering promises to bring a new level of sophistication to the field. In this analysis, we will delve deeper into the future of IVF, exploring the integration of AI, the potential for genetically tailored offspring, and the ethical implications of s-called “designer babies”.
Innovations in IVF technology
The IVF landscape has been transformed by various technological advancements, such as preimplantation genetic testing (PGT), improved embryo culture systems, and vitrification. Future innovations in IVF are expected to yield more efficient, personalized, and successful fertility treatments.
AI-driven embryo selection
AI-driven embryo selection integrates various technologies to improve the process of selecting the most viable embryos for implantation. These technologies include time-lapse imaging, machine learning algorithms, and morphokinetic analysis.
Time-lapse imaging is a non-invasive monitoring technique that captures continuous images of embryos as they develop in the incubator. This method provides a wealth of information on the embryo’s development, including cell division patterns, timing, and morphological changes. Time-lapse imaging eliminates the need for removing embryos from the incubator for assessment, minimizing the risk of damage and stress to the embryos.
Machine learning algorithms
Machine learning algorithms can analyze the wealth of data generated from time-lapse imaging and other sources, such as patient medical history and genetic screening results. By recognizing patterns and learning from the data, these algorithms can predict the embryos’ implantation potential with a higher degree of accuracy than traditional assessment methods. Machine learning algorithms can consider a wide range of factors, from morphological characteristics to the developmental timeline, to determine the embryos with the best chance of leading to a successful pregnancy.
- Morphokinetic analysis
Morphokinetic analysis focuses on the timing and patterns of cell divisions and other developmental events in embryos. By combining this analysis with AI algorithms, researchers can identify specific morphokinetic parameters that are associated with successful implantation and healthy fetal development. This method allows for a more detailed and comprehensive assessment of embryo quality and viability, leading to better-informed decisions during the selection process.
By implementing these AI-driven technologies in the embryo selection process, surrogacy agency can improve the accuracy of their predictions and increase the likelihood of successful implantations.
This has several benefits for patients, including:
Reduced number of IVF cycles: As AI-driven embryo selection increases the probability of implantation success, patients may require fewer cycles to achieve a successful pregnancy. This can significantly decrease the financial burden associated with multiple IVF treatments.
Lower emotional stress: The emotional toll of undergoing multiple IVF cycles can be significant for intended parents. By increasing the success rates of IVF treatments, AI-driven embryo selection can reduce the emotional strain associated with repeated cycles and the uncertainty of treatment outcomes.
Improved genetic screening techniques
Emerging technologies, such as next-generation sequencing (NGS) and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), have improved our ability to screen for genetic abnormalities in embryos, thereby increasing the success rate of IVF treatments.
Next-generation sequencing (NGS)
NGS is a high-throughput method that allows for the rapid sequencing of large amounts of DNA. In the context of IVF, NGS is used for Preimplantation Genetic Testing (PGT). PGT involves the removal of a few cells from an embryo, which are then sequenced using NGS to detect any genetic abnormalities.
NGS can identify single-gene disorders (PGT-M), chromosomal imbalances (PGT-A), and structural rearrangements (PGT-SR). It can provide a comprehensive chromosomal profile of the embryo, allowing clinicians to select embryos without chromosomal abnormalities for implantation. This increases the chance of successful implantation and healthy pregnancy, as chromosomal abnormalities are a major cause of miscarriage and birth defects.
CRISPR is a revolutionary gene-editing technology that can be used to add, remove, or alter specific sequences in a genome. In the context of IVF, it has the potential to not only identify but also correct genetic abnormalities in embryos.
CRISPR works by using a molecule called RNA and an enzyme known as Cas9. The RNA molecule is designed to target a specific sequence in the DNA. When the RNA molecule finds this sequence, the Cas9 enzyme acts like a pair of molecular scissors to cut the DNA at that exact spot. This cut can disable a problematic gene, or additional DNA can be inserted to correct a genetic abnormality.
Theoretically, the ability to correct genetic abnormalities before implantation could significantly increase the success rate of IVF treatments. It could prevent the transmission of inherited genetic disorders and increase the likelihood of developing a healthy fetus. However, the use of CRISPR in human embryos is currently heavily regulated due to ethical and safety concerns, including off-target effects and the potential for unintended consequences in the genome.
Artificial intelligence in genetic engineering:
AI-driven genetic engineering has the potential to revolutionize the field of reproductive medicine. AI can analyze vast genomic datasets, identify patterns, and predict the effects of specific genetic modifications. This may facilitate the creation of so-called “designer babies” – i.e. offspring with tailored traits, such as heightened intelligence, physical abilities, or disease resistance.
This latter potential raises several ethical, legal, and social concerns.
The concept of “designer babies” poses numerous ethical challenges:
- Informed consent and autonomy: Genetic modifications in embryos can have unforeseen consequences that may impact future generations. Obtaining informed consent from all stakeholders, including the future offspring and their descendants, is a complex issue that requires careful deliberation.
- Potential for social stratification: Access to AI-driven genetic engineering may be limited to those with financial means, leading to an exacerbation of social inequalities and a divide between the “genetically enhanced” and the “unmodified”.
We could draw 2 parallels with historical and societal precedents: Racial segregation in the United States in recent history, and the caste system in India.
Racial segregation in the United States, particularly during the 19th and 20th centuries, could serve as a proxy to exemplify the societal effects of selection based on immutable characteristics. Following the end of slavery, many states implemented a system of racial segregation through the enforcement of the so-called “Jim Crow” laws. These laws mandated the separation of white and black individuals in public spaces, schools, and transportation, among other areas.
Racial segregation led to a significant divide between “haves” and “have-nots,” as African Americans were systematically denied access to quality education, housing, and employment opportunities for over a century. This resulted in generational poverty, limited social mobility, and a reinforcement of racial stereotypes.
Some sociologists consider that despite the abolishment of the Jim Crow laws and the efforts deployed by governing bodies at the Federal and state level to reduce racial disparities since the Lyndon B. Johnson’s administration, the consequences of the segregation system can still be observed today in the form of persistent social and economic inequalities.
The caste system in India is another historical example of social selection based on immutable characteristics that have exacerbated social inequalities and created a divide between different groups of the population belonging to the same country.
The caste system was a traditional social hierarchy in which people were assigned to specific categories based on their birth and ancestry. This system had four main categories, known as “varnas,” further divided into numerous sub-castes, or “jatis.”
The caste system perpetuated a rigid social structure, with people in the higher castes enjoying greater access to resources, education, and job opportunities, while people in the lower castes faced discrimination, exclusion, and limited social mobility.
This divide between the “haves” (higher castes) and the “have-nots” (lower castes) has led to significant social and economic disparities with respect to:
- Education: Access to quality education varies significantly across different castes. Historically, lower castes were often excluded from formal education systems. Although various measures have been implemented to improve access to education for lower castes, including reservation quotas, significant disparities remain. For instance, literacy rates and school enrollment figures tend to be lower among lower caste groups, and dropout rates are higher compared to upper castes.
- Employment: Caste also plays a significant role in employment. Certain occupations have been traditionally associated with specific castes, especially in rural areas. Lower castes often engage in labor-intensive jobs, while higher castes tend to hold positions of power and prestige. Additionally, caste-based discrimination can limit job opportunities for lower caste individuals in both public and private sectors.
- Income and wealth: The disparities in education and employment opportunities have contributed to economic inequalities across different castes. On average, families belonging to lower castes have lower incomes, less accumulated wealth, and higher poverty rates than their upper caste counterparts.
- Health: Lower castes generally have poorer health outcomes, largely due to limited access to quality healthcare, poor living conditions, and higher rates of malnutrition.
- Social mobility: The caste system has historically limited social mobility, making it difficult for individuals from lower castes to improve their social and economic status. Although there have been notable exceptions, these are not indicative of widespread trends.
The caste system has been legally abolished in India with the 1950 Constitution prohibiting caste-based discrimination and the introduction of affirmation action measures. The Indian government adopted a different classification system to discontinue the use of the caste terminology. Terms such as “Scheduled Castes”, “Scheduled Tribes”, “Other Backward Classes” and “Economically Weaker Sections” serve as practical proxies to designate the realities of Indian sociological inequalities.
It can hardly been denied however that a system in use for millennia has shaped the collective perception of the “rightful position” of each individual in Indian society. The caste system may have been abolished, but its effects persist both in terms of socio-economic status and opportunities, and in the minds of the Indian population.
These 2 parallels show how dangerous and pernicious the consequences of a social stratification based on the anthropogenic creation of a divide between “genetically enhanced” and “unmodified”.
- Eugenics and genetic discrimination: Selecting embryos based on certain genetic traits could promote a harmful concept of genetic superiority, leading to discrimination against individuals with disabilities or other genetic variations.
It is easy to find examples of this in history. Arthur de Gobineau’s “An Essay on the Inequality of the Human Races” promoted the concept of racial superiority and the idea that certain races were inherently superior to others. Gobineau’s theories contributed to the development of racialist ideologies, which later influenced eugenics movements and racial policies in various countries.
Drawing a parallel between Gobineau’s theories and the assertion that selecting embryos based on certain genetic traits could promote a harmful concept of genetic superiority, we can observe that both situations involve the risk of fostering discrimination based on inherited characteristics. While Gobineau’s theories focused on racial differences, the assertion regarding selecting embryos addresses the potential for discrimination against individuals with disabilities or other genetic variations.
In both cases, the underlying concern is that prioritizing certain traits or characteristics can lead to the marginalization and stigmatization of individuals who do not possess those traits.
Legal and regulatory challenges
To prevent the misuse of AI-driven gene-editing technology and protect the welfare of future generations, comprehensive legal and regulatory frameworks must be established. Key considerations include:
- International harmonization of policies: Global collaboration is essential to develop universally accepted guidelines and regulations governing the use of AI and genetic engineering in IVF treatments.
But typical international organizations or regulatory bodies situate themselves outside of the purview of democratic representation. Their actors are often not elected representatives, or when they are as in the case of the EU parliament, they tend to concentrate powers that can’t be subject to the checks and balance of representative democracy and often override the electorate.
It is crucial therefore to ensure that any guidelines or regulations governing AI and genetic engineering in IVF treatments are developed with the input and scrutiny of a broad range of stakeholders, including the general public.
One approach to reconciling the need for global collaboration with the principles of representative democracy is to involve national governments and their elected representatives in the process. These representatives can participate in international discussions and negotiations, while also seeking input from their constituents and ensuring that the perspectives of their electorate are taken into account.
Another possibility is to facilitate public engagement and consultation through various means, such as surveys, public debates, and workshops. This would allow the general public to share their views, concerns, and opinions on the ethical, legal, and social implications of AI and genetic engineering in IVF treatments. Public input could then be incorporated into the decision-making processes of international organizations and regulatory bodies.
Additionally, promoting transparency in the development of guidelines and regulations can help to ensure that the process is open to scrutiny and that any decisions made are based on sound evidence and ethical considerations. This could involve making the proceedings of international meetings and negotiations accessible to the public, as well as publishing the outcomes of consultations and the rationale behind any decisions made.
Finally, the issues could also be submitted to direct referenda, with the vox populi binding the vote and action of national governments.
- Establishing oversight mechanisms: Regulatory bodies and monitoring systems must be put in place to ensure ethical practices, maintain transparency, and guarantee the safety of patients and their offspring.
Some of the key institutions that could be involved in oversight include:
Food and Drug Administration (FDA): The FDA is responsible for protecting public health by ensuring the safety and efficacy of drugs, medical devices, and biologics, among other things. It would likely play a central role in regulating AI-driven IVF technologies and genetic engineering practices, particularly when it comes to approving new treatments or devices for use in clinics.
National Institutes of Health (NIH): The NIH is the primary federal agency for conducting and supporting biomedical research in the United States. It establishes guidelines and regulations for research involving human subjects, and its Recombinant DNA Advisory Committee (RAC) oversees research involving genetic engineering. The NIH could play a role in setting research guidelines and funding priorities related to AI-driven IVF and genetic engineering.
Institutional Review Boards (IRBs): IRBs are committees within research institutions that review and approve research involving human subjects to ensure that it complies with ethical guidelines and federal regulations. These boards could be responsible for overseeing the ethical conduct of research related to AI-driven IVF and genetic engineering within their respective institutions.
Centers for Medicare & Medicaid Services (CMS): CMS is responsible for overseeing the quality and safety of medical services in the United States, including those provided by IVF clinics. They could be involved in setting and enforcing standards for AI-driven IVF technologies and genetic engineering practices.
State-level agencies: In addition to federal agencies, state-level agencies, such as Departments of Health, may also play a role in regulating and overseeing AI-driven IVF technologies and genetic engineering practices within their jurisdictions.