Advanced machine learning is poised to end fertility medicine’s decade-long success plateau, but validation gaps and regulatory uncertainty cloud the technology’s path to mainstream adoption
The fertility medicine sector faces an uncomfortable truth: despite decades of technical refinement, in vitro fertilization success rates have remained stagnant at approximately 30 percent for over a decade. This plateau has driven intense research into artificial intelligence and machine learning as potential catalysts for breakthrough improvements. Early clinical evidence suggests the opportunity is real—but whether AI fulfills its therapeutic promise depends on navigating treacherous terrain spanning clinical validation, regulatory approval, and ethical implementation
Closing the Variability Gap
IVF’s persistent success ceiling stems fundamentally from human limitation, not technical constraint. Embryologists, despite their expertise, perform embryo selection and quality assessment tasks bound by subjective visual judgment. This subjectivity introduces substantial variability—both within individual practitioners across repeated assessments and between different laboratories applying ostensibly identical grading systems.
Artificial intelligence addresses this structural problem through objective, reproducible image analysis. Systematic reviews comparing AI algorithms to clinical embryologists demonstrate consistent performance superiority. Across multiple studies, AI models achieved median accuracy of 75.5 percent (range 59–94%) in predicting embryo morphology grades, compared to 65.4 percent accuracy among trained embryologists applying identical ground-truth standards. When predicting clinical pregnancy outcomes using clinical data alone, AI models demonstrated 77.8 percent accuracy (range 68–90%) versus 64 percent for embryologists. Crucially, when AI algorithms combine time-lapse imaging with clinical patient data—a multimodal approach that mirrors how experienced clinicians intuitively synthesize information—accuracy surges to 81.5 percent for AI versus only 51 percent for human practitioners.
The most rigorous direct comparison examined the MAIA platform in routine clinical practice across three fertility centers. Analyzing over 200 embryo transfers, MAIA achieved 70.1 percent accuracy in predicting clinical pregnancy for elective transfers (cases with multiple eligible embryos), with strong correlation between MAIA predictions and actual clinical outcomes (R² ranging 0.65–1.0). In 44 cases where MAIA and embryologists disagreed about embryo ranking, MAIA’s choice proved superior 81.8 percent of the time, yielding a 75 percent clinical pregnancy rate compared to 47.4 percent when embryologists overruled AI recommendations.
Real-world clinical pregnancy rates with AI-assisted selection have reached 61–62 percent, a substantial leap above baseline IVF success, though precise attribution to AI versus other contemporary improvements remains methodologically complex.
Gamete and Ovarian Assessment
The AI revolution in reproductive medicine extends far beyond embryo selection. Sperm analysis, historically dependent on manual microscopy with acknowledged limitations in reproducibility and accuracy, has emerged as another critical application domain. Conventional semen analysis assesses sperm concentration, motility, and morphology according to WHO guidelines, but provides limited insight into functional fertilization capacity. Only approximately 7 percent of ejaculated sperm possess genuine fertilization potential—a critical distinction that traditional analysis overlooks.
The University of Hong Kong recently unveiled a breakthrough AI model that analyzes sperm morphology specifically to predict binding capacity to the zona pellucida (the egg’s outer coat), the crucial first step in fertilization. This AI model, trained on over 1,000 sperm images and subsequently validated against 40,000 sperm images from 117 infertile men, achieved clinical validation accuracy exceeding 96 percent. The model identifies a clinical threshold of 4.9 percent zona pellucida-binding sperm; men below this threshold face elevated fertilization failure risk despite apparently normal conventional semen parameters. The innovation’s clinical significance lies not merely in accuracy but in standardization—automated sperm assessment eliminates the 20–40 percent variation that exists between individual technicians performing manual microscopy.
Equally transformative advances address oocyte (egg) quality assessment. VIOLET, an AI system developed using convolutional neural networks trained on 17,659 oocyte images, predicts fertilization and blastocyst formation with 91.2 percent and 63 percent accuracy respectively. In head-to-head comparison against 17 embryologists from eight different clinics, VIOLET outperformed all embryologists by an average of 21.8 percent in fertilization prediction and 20.2 percent in blastocyst development prediction. Remarkably, while embryologist accuracy remained essentially unchanged when reassessed 2–3 months later (approximately 53% ± 3.3%, suggesting assessment relies heavily on chance), VIOLET demonstrated 100 percent reproducibility.
From Stimulation to Transfer
Beyond gamete and embryo assessment, AI is reshaping IVF protocol optimization. Controlled ovarian hyperstimulation—the carefully calibrated drug dosing protocol intended to retrieve an optimal number of mature eggs—remains highly variable across clinicians despite standardized protocols. Machine learning models trained on historical data from 30,000+ cycles now predict optimal gonadotropin starting doses based on patient baseline characteristics including anti-Müllerian hormone (AMH), antral follicle count (AFC), and body mass index (BMI).
These AI-guided dose selection systems achieve dual objectives: they minimize gonadotropin exposure (reducing medication burden, cost, and side effects) while maintaining or improving mature oocyte yield. Critically, AI-derived trigger timing recommendations for final oocyte maturation have demonstrated association with improved oocyte maturity and subsequent embryo development compared to physician-determined timing. Fully integrated systems tracking follicular development and hormone levels in real-time provide personalized recommendations that would be logistically impossible for clinicians to calculate manually.
The AACS (AI Asada-style Controlled Ovarian Stimulation support system) implemented at a Japanese fertility center demonstrates this approach’s clinical utility. The system comprises two models: an oocyte retrieval decision model predicting optimal retrieval timing, and a prescription inference model recommending drug dosages based on patient-specific follicular and hormonal status. Both models achieved high accuracy in clinical implementation, and the system has been integrated into routine clinical workflow.
Ensemble machine learning approaches combining multiple algorithms further enhance predictive performance. AdaBoost combined with genetic algorithm feature selection achieved 89.8 percent accuracy in predicting IVF success, with Random Forest reaching 87.4 percent. These models identified ten key features influencing IVF outcome: female age, AMH, endometrial thickness, sperm count, sperm morphology, follicle size, retrieved oocyte number, oocyte maturity quality (MII), and embryo quality.
Market Expansion and Regulatory Inflection Points
The commercial market trajectory reflects growing clinical confidence. The AI-powered embryo selection market is projected to expand from USD 183.4 million in 2025 to USD 537.2 million by 2035, representing an 11 percent compound annual growth rate. This expansion reflects both technological advancement and increasing adoption—global surveys document that over 50 percent of fertility specialists now report regular or occasional AI usage as of 2025, up dramatically from 2022 baseline adoption rates.
India represents a critical inflection point in this global expansion. In December 2025, Aansh Hospital in Maharashtra announced deployment of Garbha.ai, marking India’s first CDSCO-approved AI platform specifically designed for fertility treatment. This regulatory milestone holds substantial significance: many fertility clinics globally utilize imported AI tools carrying FDA or CE (European) approvals, but lack rigorous local validation according to Indian patient populations and treatment protocols. Garbha.ai’s CDSCO approval signifies that India’s regulatory framework is crystallizing around standardized AI validation requirements for reproductive medicine.
Clinical outcomes reported from Aansh’s Garbha.ai implementation include 94 percent accuracy in embryo quality grading and 25 percent higher implantation success rates in first-cycle transfers—metrics substantially above conventional methods. The platform employs a “full-stack” approach integrating embryo selection, ovarian response prediction, endometrial receptivity analysis (ERA for optimal transfer timing), and pharmacogenomic drug optimization (PGx for personalized gonadotropin dosing).
Validation Challenge
Despite promising clinical applications, substantial evidence gaps threaten the field’s trajectory. A 2025 global survey found that 32.16 percent of fertility specialists remain uncertain about AI accuracy for embryo selection due to insufficient clinical evidence. This skepticism is methodologically justified.
Current AI performance metrics, while superior to human assessment, remain substantially below clinical perfection. Diagnostic meta-analysis of AI embryo selection systems revealed pooled sensitivity of 0.69 and specificity of 0.62 across multiple studies—indicating that AI correctly identifies viable embryos 69 percent of the time while incorrectly classifying 38 percent of non-viable embryos as viable (false positive rate). The positive likelihood ratio of 1.84 means AI-selected embryos are approximately 2 times more likely to result in pregnancy than non-selected embryos, but this still leaves substantial pregnancy risk even with AI-guided selection.
Furthermore, critical methodological limitations plague current validation studies. The vast majority of published research employs retrospective study designs, where AI performance is assessed against historical embryo images with known outcomes. This retrospective approach introduces substantial bias; prospective validation—where AI recommendations inform actual clinical decisions in real-time—remains sparse. The few prospective studies demonstrate materially lower AI performance than retrospective studies, suggesting potential overfitting to historical training datasets.
Data consistency and model reproducibility present additional challenges. Recent research found extremely poor consistency in how different AI models rank the same embryos (Kendall’s W ≈ 0.35, where 1.0 would indicate perfect agreement). This suggests that while individual AI models may perform well, the field lacks standardization in training methodologies, data preprocessing, and algorithmic architecture—meaning a clinic adopting one embryo selection AI system may not achieve equivalent results if switching to an alternative platform.
Additionally, most AI validation studies employ surrogate outcomes such as blastocyst formation or clinical pregnancy detection. Clinical outcomes that truly matter to patients—live birth rates, time to pregnancy, cumulative success across multiple cycles—remain understudied in AI literature. Current studies demonstrate AI’s ability to predict morphology-based outcomes and short-term pregnancy detection, but evidence linking AI-guided selection to improved long-term reproductive success remains limited.
Ethical Frontiers
AI implementation in reproductive medicine raises profound ethical challenges that regulatory frameworks have not yet adequately addressed. The most immediate concern involves deskilling of embryologists and embryologists. As AI algorithms increasingly take primary responsibility for embryo selection, the clinical experience accumulated through years of manual assessment becomes redundant—threatening the professional development and job security of laboratory staff. This raises the question: if AI systems replace human decision-making in embryo selection, how do new embryologists develop the tacit expertise required to identify edge cases where AI recommendations might be questionable?
Algorithmic bias represents another fundamental concern. AI systems trained on retrospective data reflecting historical clinic practices may perpetuate or amplify existing disparities. For instance, if historical data disproportionately represents younger women (who traditionally had better IVF success), the AI system might learn sub-optimal treatment recommendations specifically for older patients. The absence of diverse training datasets across different ethnic backgrounds, geographic regions, and socioeconomic statuses means that current AI models may not perform equally well for all populations.
Transparency and accountability gaps create additional complexity. Many commercially deployed AI systems operate as “black boxes,” where even developers cannot fully explain the specific features driving individual predictions. This creates profound tension with informed consent: how can patients understand the reasoning behind AI-recommended embryo selection when the algorithm itself cannot articulate its decision-making logic? Furthermore, if an AI-selected embryo fails to implant or results in adverse outcome, unclear accountability mechanisms create moral and legal ambiguity—is responsibility attributable to the AI developer, the clinic using the system, the embryologist, or the AI algorithm itself?
The potential for sex-selection misuse represents a final ethical boundary. While AI-assisted embryo selection for medical reasons (such as sex-linked genetic disease prevention) enjoys broad ethical support, the technology could theoretically facilitate discriminatory sex selection for family balancing or cultural preference—creating particular concern in countries with known sex-ratio imbalances.
India’s Dual Opportunity & Challenge
The Indian fertility medicine landscape presents a paradoxical context for AI adoption. India accounts for approximately one-third of the global infertility burden while simultaneously developing world-class fertility infrastructure in metropolitan centers. This creates both opportunity and ethical risk.
Opportunity stems from India’s rapidly expanding fertility sector and the acute need to standardize quality across widely variable clinic infrastructure. India’s ART Act and ART Rules (2022–2024) mandate strict documentation, digital record-keeping, and traceability for assisted reproductive procedures. AI systems that integrate with electronic medical records and laboratory management systems can facilitate compliance while simultaneously improving clinical outcomes. Regulatory frameworks such as ABDM (Ayushman Bharat Digital Mission) and DPDPA (Data Protection Act 2023–24) provide scaffolding for standardized data governance.
However, substantial barriers hinder adoption. A 2024–25 Economic Survey identified that AI adoption in Indian healthcare faces critical constraints including scarcity of specialized technical and domain-specific talent, data complexity, and difficulties in scaling deployment beyond tertiary centers. The high initial infrastructure costs of AI systems create particularly acute barriers for mid-tier fertility clinics serving India’s rapidly growing middle-income population—potentially widening equity gaps between world-class metropolitan centers and regional providers.
The Path to Responsible Implementation
The trajectory of AI in fertility medicine hinges on coordinated action across multiple domains. First, the field urgently requires standardized multicenter prospective validation studies with live birth as the primary outcome, not surrogate markers. These trials must include diverse patient populations across geographies, ethnic backgrounds, and socioeconomic statuses to ensure AI models generalize beyond their training cohorts.
Second, professional organizations including ESHRE, ASRM, and national regulatory bodies including India’s CDSCO must establish minimum performance benchmarks for AI system approval and post-market surveillance protocols to detect performance drift over time. The CDSCO’s recent approval of Garbha.ai demonstrates that governments can move swiftly when clear clinical evidence exists; accelerating this process while maintaining rigor is essential.
Third, transparent governance frameworks must articulate clear accountability mechanisms. When AI recommendations inform clinical decisions, responsibility must be explicitly allocated among developers, clinics, embryologists, and regulators—not left ambiguous.
Fourth, training standards and certification pathways must equip embryologists with hybrid skillsets combining AI literacy with domain expertise. Rather than automation eliminating human expertise, the optimal model involves human-AI collaboration where embryologists understand AI capabilities and limitations while maintaining ability to identify cases requiring human judgment.
Finally, equitable access frameworks must ensure that AI adoption does not concentrate superior outcomes among wealthy metropolitan centers while leaving underserved populations with conventional methods. This requires intentional policy action, including consideration of subsidized AI access in public healthcare systems and regional fertility centers.
Promise Tempered by Pragmatism
Artificial intelligence represents a genuine clinical opportunity to end IVF’s decade-long success plateau. Evidence of 15–20 percent improvement in pregnancy rates, when combined with 94 percent accuracy in morphological assessment and the potential for optimization across the entire treatment cycle, demonstrates substantial clinical utility.
Yet the path from laboratory promise to mainstream clinical benefit remains contingent on closing significant gaps. Large prospective validation studies with diverse populations, standardized regulatory frameworks, transparent governance, and intentional safeguards against bias must precede uncritical adoption. The technology itself is advancing rapidly; the institutional structures to ensure responsible deployment have yet to fully crystallize.
India’s CDSCO approval of Garbha.ai in late 2025 signals that regulatory frameworks are evolving. This milestone, combined with the substantial fertility medicine infrastructure being built across Indian metropolitan centers, positions the country as a critical testing ground for responsible AI implementation at scale. Whether India’s fertility sector becomes a model for equitable, transparent AI governance—or instead exemplifies the widening divides that emerge when powerful technologies are deployed without adequate oversight—will substantially influence global trajectories in reproductive medicine.Dr. Sri Nayana Kavuri
