Skip to main content
Fertiligent AI logoFertiligent AI

Infertility in the Age of the Singularity

This report was originally published in Fertility Intelligence, Fertiligent™'s newsletter on the demographics, science, and economics shaping how we build families. It is reproduced here with light editing for the web.

Executive Summary

Infertility affects roughly 1 in 6 people worldwide[1], amid historic fertility-rate declines (global TFR ~2.3 in 2023[2]). Demand for assisted reproductive technologies (ART) is surging: ~2.5 million IVF/ART cycles now yield ~500,000 births annually[3][4], with the fertility-services market projected to grow from ~$42B (2023) to ~$70B by 2030[5].

Infertility has multifactorial causes – roughly one-third male factor, one-third female, and one-third combined or unexplained[6] – diagnosed via hormone assays, semen analysis, ultrasound/imaging, and genetic tests (e.g. karyotyping, PGT-A). Existing ART includes IVF/ICSI, cryopreservation (eggs/embryos via vitrification), and embryo-selection methods (morphological grading, time-lapse imaging, genetic screening).

Technological drivers now include AI and automation in IVF labs, advanced embryo assessment, personalized stimulation protocols, and emerging biotechnologies (e.g. in vitro gametogenesis, advanced cryotech). For example, deep-learning models on time-lapse embryo videos can predict pregnancy and chromosome-normality with ~0.75 AUC[7]. Robot-assisted systems (e.g. Conceivable's AURA) can automate fertilization steps end-to-end[8]. Novel "wetware" data centers (Cortical Labs' CL1) use networks of lab-grown human neurons to compute, promising high-efficiency AI platforms[9].

Clinical implications include potentially higher IVF success rates (AI embryo selection outperforms human labelling[10]), more informed protocols, and fully digitized patient pathways. AI agents integrated with electronic medical records (EMRs) could automate chart reviews, patient counseling, and personalized cycle planning. Business models and market structure are evolving: the field is highly fragmented with many local clinics[11], largely consumer-pay in most countries (WHO notes catastrophic OOP costs in absence of coverage[12]), but many employers increasingly offer fertility benefits[13]. Direct-to-consumer tests (e.g. home fertility hormone kits) and telehealth consults are proliferating.

Ethical, legal, and social issues are substantial: AI-driven embryo selection raises concerns of "dehumanization," bias, lack of transparency and explainability[14]; expanded embryo screening or in vitro gametogenesis could spur new eugenics debates[15]; consent and equity issues loom as reproductive care is costly and unevenly accessed. The regulatory landscape is uneven: China's NMPA has now approved a commercial invasive BCI implant to restore hand movement[16][17] (a technology tangentially relevant to neuromodulation of reproductive health), but still bans germline-editing pregnancies[18]. The US largely relies on state insurance mandates and the FDA (currently banning U.S. embryo editing trials), while EU countries vary widely (e.g. France/Germany are restrictive, UK's HFEA tightly regulates all embryo work). Risks and failure modes range from technical (misclassification of embryos, AI overreliance, data breaches) to societal (discrimination, widening access gaps, ethical misuse of genetic data).

Recommendations: Stakeholders should proactively integrate AI with rigorous validation; expand equitable insurance and employer coverage; strengthen informed consent and privacy safeguards; update regulations for AI and genetic tools; and fund public education on fertility options. In sum, fertility care stands at a technological inflection point: strategic policy and clinical initiatives are needed to harness AI/biotech benefits while safeguarding rights and equity.

Figure: Total fertility rate (births per woman) has declined worldwide. Most regions have fallen to or below replacement level (~2.1)[2].

Global fertility rates have roughly halved since the mid-20th century[19]. In 2023, the world-average total fertility rate is ~2.3 children per woman[2] (see figure above), far below the postwar peak (~4.9 in 1960s[2]). Fertility has dropped fastest in East Asia and Europe (South Korea ~0.8, Japan ~1.3; see above) and is declining in much of Africa and Latin America as well[2][19]. This demographic shift means more couples face involuntary childlessness. Indeed, WHO reports that ~17.5% of people (≈1 in 6) will experience infertility (failure to conceive after ≥12 months) in their lifetimes[1]. Prevalence is similar in high- and low-income countries (17–18% vs. 16–17%[1]). For context, about 10% of U.S. women (ages 15–44) are infertile at any given time[20].

Causes and Data: Infertility has multiple causes. Roughly one-third of cases are due to female-factor issues (ovulatory disorders like PCOS, age-related ovarian decline, tubal damage from infections or surgery, uterine factors)[6], one-third to male factors (low sperm count or motility, genetic abnormalities, endocrine disorders)[6], and the remainder are mixed or unexplained[6]. Diagnostics draw on diverse data types: endocrine labs (FSH, LH, estradiol, AMH, prolactin), genetic/karyotype tests, imaging (transvaginal ultrasound, hysterosalpingogram, MRI for structural issues), and semen analysis for males. In ART, embryo assessment adds its own data stream (microscope images, time-lapse videos, and increasingly, AI-derived image features). Together, these clinical data form a rich patient profile that modern systems can ingest.

Existing ART Technologies: Since 1978, when the first IVF baby was born, IVF has become a staple of fertility care. Over 10 million children have now been born via IVF/ART globally[3]. Currently ~2.5 million ART cycles are performed worldwide each year, yielding over 500,000 live births annually[3][4]. Innovations include intracytoplasmic sperm injection (ICSI) for severe male-factor infertility, refinements in ovarian stimulation protocols, and widespread use of cryopreservation. Egg and embryo freezing by vitrification is now routine, enabling egg banking and deferred childbearing. Preimplantation genetic testing (PGT-A) and embryo "hatching" techniques improve selection, though PGT-A's net benefit is debated. Time-lapse incubators allow continuous embryo monitoring. In short, today's standard ART toolkit includes IVF/ICSI, highly optimized stimulation protocols, cryopreservation of gametes/embryos, and enhanced embryo-selection methods (morphological scoring and genetic screening) to maximize the ~30–40% per-cycle success rates[3][4].

Technological Drivers

AI and Computer Vision: A surge of AI research promises to optimize almost every step of fertility care. A prime example is AI-powered embryo selection. Deep learning models using time-lapse videos of developing embryos can predict implantation success and chromosomal normality. For instance, Maekawa et al. (2026) trained convolutional neural nets on 2,400+ embryos and achieved AUC≈0.75 for predicting pregnancy and embryo ploidy[7]. Systematic reviews likewise find that machine-learning models consistently outperform embryologists in predicting pregnancy from embryo morphology[10]. In practice, commercial tools (e.g. Life Whisperer, EMG/ALPHA, others) and research prototypes are being deployed: early evidence shows AI can standardize grading, reduce error, and shorten evaluation time[21][22]. Beyond embryos, AI can tune stimulation protocols by mining hormonal and historical cycle data, forecast patient response, and personalize drug dosing. Voice-based agents or chatbots could interact with patients (scheduling, answering FAQs) and even paraphrase their complex medical histories. Integration with EMRs would allow AI assistants to synthesize a patient's full history, lab trends, and genetic results to recommend tailored interventions.

Robotics and Lab Automation: Robotic systems are entering the IVF lab to reduce human labor and variability. For example, Conceivable (formerly Creosalus) has built an "AURA" robotic IVF assembly line that automates fertilization steps (micromanipulation, incubator transfer, etc.)[8]. Automated liquid-handling robots can pipette gametes and media under controlled conditions, while microfluidic chips sort and concentrate sperm. Such automation boosts throughput and consistency – important given rising demand and limited skilled embryologists. Reports indicate first IVF cycles managed by robotic labs are already underway, and more systems are in development. Physical lab environments may become "lights-out" with robots performing culture, media changes, and transfer under AI supervision.

Biotechnology and Bio-innovation: Beyond AI and robots, new biological technologies are poised to transform fertility. In vitro gametogenesis (IVG): Generating eggs or sperm from stem cells is still experimental, but moving towards reality. A 2023 National Academies workshop reviewed efforts to create human gametes from iPS cells[23]. If achieved, IVG could allow infertile individuals (e.g. same-sex couples or people with ovarian failure) to have genetically related children by turning somatic cells into eggs/sperm. Although no functional human gametes have been produced yet, partial successes in animal models suggest IVG is worth monitoring. This could radically change ART by obviating the need for egg donation or severe ovarian stimulation[23]. Advanced Cryopreservation: New methods may improve preservation of ovaries or even whole reproductive organs for future use. (In parallel fields, mouse hippocampus has been revived after vitrification[24] – hinting at future whole-organ cryobanking, though far off.) Regenerative medicine: Uterine transplants are now a clinical reality, and bioengineered uterine tissue or artificial wombs are under research. Even neural devices like the new Chinese brain-computer interface (BCI) for paralysis[16], while not directly a fertility tech, exemplify the rapid bio-digital integration that may one day intersect with reproductive health (e.g. neuromodulation of endocrine axes, or memory support during fertility counseling). Biological computing: Unconventional computing platforms (e.g. Cortical Labs' neuron-on-chip "wetware"[9]) are emerging. Such systems could potentially simulate complex biological processes (like embryo development) more efficiently, or provide new AI compute backends for fertility data analysis.

Comparative technologies: A summary of these key technologies — their potential impact on fertility care, current maturity, and the main players and evidence behind each — runs across the sections above.

Clinical Implications

The convergence of these technologies will reshape fertility care.

Improved outcomes: AI-enhanced embryo selection and personalized protocols could raise IVF success rates (currently ~30–40% per transfer). For example, ML tools have already achieved 75–94% accuracy in identifying high-quality embryos vs. human grading[21]. Clinics adopting these tools may see fewer cycles per pregnancy.

Workflow changes: Routine tasks (embryo grading, cycle documentation) may be handled by AI/robots, reducing workload for embryologists. Patients might interact with digital assistants: an AI chatbot could triage a patient's hormone levels and prior history, suggesting tailored diet or stim protocols, or remind about medications and appointments.

Data integration: Fertility EMRs (like those by OutcomeMD, Meditrina, MedArt) already collect rich data. Connecting them to AI will enable insights from aggregating thousands of past cycles. For instance, an AI might detect that a certain protocol historically yields 20% better outcomes for 38-year-old patients with low AMH, then advise that protocol.

Personalization: Integrative "digital health" approaches (wearables tracking cycle physiology, microbiome profiling, etc.) could modulate fertility care. The Stanford gut-brain study[27] suggests that systemic factors (microbiome, vagal tone) affect cognitive and possibly hormonal axes – in the future, fertility treatments might consider such holistic factors too.

Importantly, these advances should enhance but not replace clinical judgment. AI can flag promising embryos, but clinicians will remain responsible for patient care. Transparency and explainability will be needed: clinics must understand why an AI made a recommendation. As the Monash bioethics review notes, trust and ethics hinge on managing issues like dehumanization, bias, and accountability[14].

Business and Market Opportunities

The fertility market is large and still growing. Grand View Research (2024) estimates the global fertility services market at ~$42.2 billion (2023), with a CAGR ~7.5% to $70.3 billion by 2030[5]. Growth is driven by aging populations in developed countries, cultural shifts delaying childbirth, and rising infertility diagnoses[1][5]. Regions vary: North America holds a large share (fueled by insurance mandates in some states), Europe has extensive IVF usage, and Asia-Pacific (notably China, Japan, Korea) is rapidly expanding ART infrastructure[5][11].

Providers: The field is highly fragmented[11]. Thousands of independent clinics offer IVF worldwide, ranging from large hospital-affiliated programs to small private labs. Major fertility services companies and chains (IVF Centers of Excellence, Bridge to Life, etc.) are growing, often through mergers. Private equity and industry (Merck, CooperSurgical, Illumina) are investing in reproductive health.

Payers: In many countries, ART is mainly self-pay, since insurance often excludes infertility. WHO notes many patients pay catastrophically out of pocket[12]. Where insurance covers fertility (e.g. some U.S. states, UK NHS, select employers), utilization rises. Employers increasingly see fertility benefits as recruitment tools. Surveys show ~40% of U.S. employers now offer fertility coverage (up from 30% in 2020)[13], and many include IVF and egg-freezing. In Canada, uptake is slower, though growing (only a few percent of employers cover IVF fully).

Consumers: Direct-to-consumer companies are expanding: home fertility test kits (saliva AMH tests, at-home semen analyzers), mobile apps for ovulation and cycle tracking, and telemedicine platforms for fertility consults. These tap patients earlier in the fertility journey. Cross-border "reproductive tourism" also persists in regions with restrictive laws or long wait times.

Emerging Models: Beyond clinics and insurers, novel models are arising. For example, "reproductive health platforms" integrating telehealth, in-home diagnostics, and coordinated care (e.g. Progyny, Carrot). Companies may bundle fertility with overall women's health services. There is potential for subscription models (membership IVF) or value-based care (insurers paying per live birth).

Advanced fertility technologies raise profound questions.

Consent and autonomy: Complex AI recommendations and embryo manipulations (e.g. genetic testing) must be clearly explained to patients so they can consent. Data privacy is critical: fertility records include intimate genomic and hormonal data. Who owns that data (patients, clinics, AI vendors)? There are risks of insurance or employers misusing fertility/genetic information (e.g. preemptive discrimination).

Equity: High costs risk exacerbating disparities. Wealthy individuals will access cutting-edge ART (e.g. IVG, gene editing) long before poorer people do. National health policies must consider fairness: for instance, the NHS has strict IVF limits despite demand, whereas some wealthy nations fund broad access.

Surveillance and dehumanization: The use of AI shifts decision-making from humans to algorithms. Ethicists worry this could "dehumanize" reproduction[14]. For example, parents might be uneasy if an opaque AI decides which embryo to implant. Algorithmic bias is another concern: if training data lack diversity, AI might perform poorly for underrepresented groups (e.g. embryos from older or non-European ancestry donors). Transparency ("explainability") is thus ethically important[14].

Enhancement vs therapy: Where is the line between treating infertility and "enhancing" progeny? IVG and expanded embryo screening could enable selection of preferred traits. The NASEM report notes that vastly expanding embryo production for selection raises eugenic-type concerns[15]. Society must debate which uses are acceptable (e.g. preventing lethal genetic disease vs. selecting for non-medical traits).

Reproductive rights: Empowering individuals to have children is a fundamental right; fertility tech can expand these rights (e.g. single people, LGBTQ+ couples using ART). But conflicts arise: some advocate limits (due to ethical/religious beliefs) vs. others pushing for universal access. Policy must balance respecting values with avoiding coercion or restriction of personal choice.

Regulation of emerging tech: Ethical use of gene editing or IVG will require new oversight. For example, international calls for a moratorium on germline editing came after the 2018 CRISPR-baby case. Courts may face cases on liability if AI miscarries embryos or if "wrongful life" claims arise over AI-chosen embryos.

Regulatory Landscape

Regulation of fertility technologies is highly jurisdiction-dependent. In the United States, there is no single federal ART law: the FDA regulates drugs (e.g. hormones), and the "Dickey-Wicker" amendment (House riders since 1995) prohibits federal funds for creating or destroying embryos in research. The FDA currently requires any embryo-related product (like gene-editing of embryos) to undergo clinical trial review – effectively banning clinical germline editing. Many states, however, have mandates requiring private insurers to cover infertility diagnosis or treatment to varying degrees.

In the European Union, rules vary by member state. The EU's Tissue & Cells Directive sets quality standards for gamete banks. Countries like France, Italy and Germany strictly regulate IVF (often requiring medical necessity or prohibiting egg donation/PGT), whereas Spain and the UK are more permissive. The UK's Human Fertilisation and Embryology Authority (HFEA) tightly controls all embryo work and has banned implantation of gene-edited embryos, though it allows mitochondrial donation. Preimplantation genetic testing is legal for medical reasons in most of Europe.

In China, regulations have tightened after scandals: current rules allow gene editing research but forbid implanting genetically modified embryos[18]. The recent NMPA approval of an invasive brain-computer implant for paralysis[16][17] shows China fast-tracking some neurotechnologies. China also has insurance pilots for fertility coverage in big cities.

In Japan, ART is strictly regulated: third-party reproduction (e.g. surrogacy, donor eggs) is largely banned, and IVF cycles are limited (many patients self-fund treatment abroad). Gene editing is prohibited under ethics guidelines.

Globally, AI-specific regulation is emerging but not mature. No country yet has fertility-specific AI laws; reproductive clinics must rely on medical device or data protection rules. Countries like the US and UK have begun regulating medical AI (FDA, MHRA frameworks) which will indirectly cover fertility AIs.

The regulatory gap is stark in some areas: for example, there is currently no international oversight on how fertility AI algorithms are trained or validated. Professional societies (ESHRE, ASRM, CMAJ) are discussing guidelines, but governments have not yet set formal standards for reproductive AI or advanced biotechnologies like IVG.

Risks and Failure Modes

Every new technology brings risks.

Technical errors: AI models can err if fed bad data or if applied beyond their scope. A misclassification of an embryo could lead to transferring a non-viable embryo or discarding a viable one. Overreliance on AI could deskill embryologists[14]. Robotics in the lab could malfunction or mis-handle cells if not properly calibrated.

Data/privacy breaches: Fertility clinics collect sensitive genetic and reproductive data. Cyberattacks on EMRs (already a healthcare issue) could expose personal information or even lineage data.

Medical and ethical failures: Pushing limits (e.g. extreme egg- or embryo-editing) without full understanding could harm patients or offspring. Unintended consequences of gene-editing (mosaicism, off-target effects) remain real risks. Fertility tourism to loosely regulated jurisdictions may increase (as with earlier surrogacy trends), raising potential for exploitation or substandard care.

Social consequences: Uneven access to advanced tech could create "reproductive divide": only the wealthy or technologically advanced nations offer cutting-edge solutions. Cultural backlash is possible if technologies (like IVG or embryo selection) outpace public consensus.

Systemic risks: As fertility becomes more data-driven, there is risk of "algorithmic reproduction" where parent choices are guided by market-driven AI services, raising philosophical questions about autonomy.

Recommendations

For Clinicians: Embrace validated AI tools to improve outcomes, but maintain human oversight. Train staff in interpreting AI outputs. Use AI mainly for decision-support, not replacement. Invest in data hygiene (consistent record-keeping) so AI has quality input. Maintain transparency with patients about new tech risks and uncertainties.

For Policymakers: Expand insurance coverage to prevent cost from barring treatments. Ensure regulations keep pace: update medical device laws to include AI in IVF, set standards for data security in reproductive health. Fund longitudinal studies on outcomes of AI-driven ART. Encourage international collaboration (e.g. WHO guidelines) on embryonic bioethics.

For Industry: Develop technologies with equity in mind. Provide affordable versions of AI tools for smaller clinics. Conduct rigorous clinical trials and publish results. Safeguard patient data, and be transparent about how AI algorithms were trained.

For Society: Promote public dialogue about ethical boundaries (e.g. via bioethics boards or citizen juries). Protect reproductive rights while ensuring ethical use of enhancements.

In conclusion, we are nearing a new paradigm in fertility care: AI and biotech are making what was once impossible conceivable. But the stakes are human lives and deeply personal dreams of parenthood. By combining technological innovation with foresight in ethics, law, and equitable policy, we can guide "Infertility in the Age of the Singularity" toward a future that empowers families rather than unnerves them.

Sources

Sources: WHO (2023) fertility survey[1][12]; NICHD/NIH infertility fact sheets[6]; Sciorio et al. (2025) on time-lapse and AI[28][3]; Vollenhoven et al. (2023) systematic review[10]; Maekawa et al. (2026) Sci Rep.[7]; Bloomberg news (2026) on IVF robotics[8]; Stanford Med News (2026) on gut-brain memory[29]; Data Center Dynamics (2026) on Cortical Labs neurons[9]; Reuters (2026) on China BCI approval[16][17]; Benefits Canada (2023) on employer fertility benefits[13]; National Academies (NASEM 2023) IVG workshop summary[23][15]; Koplin et al. (Monash Univ. 2024) on AI ethics in IVF[14]; Genetic Literacy Project (2020) on China gene-editing rules[18].

See it in action: Book a demo of Fertiligent's AI solutions, or try Eva, the patient companion.

Related:

Sergei Gorlovetsky, CEO, Fertiligent

Recent posts

Patient-support infrastructure for scaling fertility clinics

Patient-Support Infrastructure: How Fertility Clinics Scale Without Adding Headcount

Fertility clinics can't scale by adding clinics alone — staffing is constrained and demand is rising. Here's why patient-support infrastructure, not headcount, is becoming the real driver of sustainable growth.

Trust and understanding in healthcare AI for fertility care

Why Healthcare AI Works in Fertility Care: Trust, Understanding, and Emotional Intelligence

Advanced technology alone doesn't win adoption in fertility clinics. What actually works is AI that clinics can trust, that patients understand, and that feels emotionally present during one of life's most uncertain journeys.

Fertility data sovereignty and healthcare AI infrastructure

Fertility Data Sovereignty: The Most Sensitive Data in Medicine Meets the Least Sovereign Infrastructure

Under oath before a French Senate inquiry, Microsoft France could not guarantee that French citizens' data would stay beyond the reach of US authorities. That admission — and 23andMe's bankruptcy sale of 13 million genomes — expose why fertility clinics, which hold the most sensitive data in medicine, must control where AI inference actually happens.