Fertility is deeply personal. Every consultation, every test result, every decision carries real emotional weight, and you deserve clarity at each step. If you are exploring IVF or already on that path, you are likely doing everything you can to make informed choices, and that effort matters.
You have probably seen fertility clinics mentioning “AI-powered IVF” or “artificial intelligence for better success rates.” When you are considering IVF, those claims can make you feel excited and confused. What does AI in fertility treatment actually do during a cycle? Where does a machine’s assessment end and a doctor’s judgement begin?
Navigating fertility treatment is complex enough without having to decode marketing language on top of it. Here is what AI in fertility treatment genuinely looks like in practice, what other technology matters just as much, and what questions are worth asking before you trust any clinic’s claims.
Key Takeaways
- AI supports fertility specialists with embryo assessment, egg quality evaluation, and treatment personalisation. Your doctor always makes the final clinical decision.
- One of the most studied applications is AI embryo selection, where algorithms may help identify embryos with stronger development potential.
- Tools such as Violet AI egg quality assessment offer new ways to evaluate eggs non-invasively, an area where standard methods have traditionally been limited.
- Traceability systems like RFID-based sample tracking are equally important, ensuring the right eggs, sperm, and embryos are matched to the right person at every stage.
- Artificial intelligence IVF applications are growing in India, and outcomes are strongest when technology works alongside your individual biology and clinical expertise.
Why Fertility Clinics Are Talking about AI Right Now
Over the past five years, machine learning tools trained on hundreds of thousands of embryo images have moved from research labs into clinical use, with a 2025 review in Patterns (PMC) confirming that AI in assisted reproduction is rapidly advancing.
At the same time, per-cycle IVF success rates vary widely depending on age and individual factors, and the World Health Organisation’s 2023 report placed the global prevalence of fertility difficulties at roughly 17.5%. Clinics are actively seeking ways to improve outcomes, and AI is the most visible new tool available.
“AI-powered” is a broad label, though. Some clinics use validated, certified AI tools integrated into their embryology workflow, while others may use the term loosely. Understanding what AI actually does helps you tell the difference.
What Violet AI Actually Measures in Egg Quality Assessment
Egg quality is one of the most important factors in IVF outcomes, yet it has historically been one of the hardest to measure. Unlike embryos, eggs tend to look quite similar to the human eye, which is exactly where AI-assisted tools add the most value.
Violet, developed by Future Fertility (a Canadian biotech company), uses machine learning to assess egg quality non-invasively (without touching or altering the egg) from a single microscope image. According to the company, Violet can predict whether an egg will fertilise approximately 90% of the time. Future Fertility’s AI platform is trained on over 650,000 egg (oocyte) images and deployed across more than 300 clinics in 35 countries.
Violet measures structural features invisible to the human eye, such as the texture of the egg’s inner material (cytoplasm), its protective outer shell (zona pellucida), and the small cell released during maturation (polar body). For someone going through IVF or considering egg freezing, an objective egg quality score helps set more informed expectations, and Violet scores give your embryologist an additional data point alongside their broader assessment.
How AI Supports Embryo Selection, and Why Your Doctor Still Leads
Selecting the right embryo for transfer is arguably the most consequential decision in an IVF cycle. Conventional selection relies on visual grading by embryologists, and AI now gives these specialists an additional layer of objective data to work with.
How AI-Assisted Embryo Ranking Works
In AI embryo selection, algorithms compare an embryo’s visual characteristics and growth timeline against data from thousands of previous cycles, ranking embryos by estimated viability and often providing a probability score for implantation.
A systematic review in Human Reproduction Open (PMC, 2023) found that AI consistently matched or outperformed clinical teams in embryo morphology assessment across 20 studies. A 2025 meta-analysis in Contraception and Reproductive Medicine (PMC) confirmed that AI-based selection tools show potential to enhance outcomes, with ongoing trials expected to strengthen this evidence.
Some AI tools being adopted in Indian fertility clinics include:
- ERICA (Embryo Ranking Intelligent Classification Algorithm): Ranks embryos based on euploidy (correct chromosome number) and implantation potential.
- Vita Embryo (by Kai Health): An AI assessment tool with medical device certification in India, Europe, Singapore, and South Korea. According to the company, it may improve selection accuracy, with independent peer-reviewed studies underway to further validate its performance.
How your clinical team stays in charge
AI provides rankings and scores, and your embryologist and fertility specialist review those results against your clinical picture before making the transfer decision. Every certified AI tool in current clinical use is designed to inform your specialist, keeping the final call in expert hands.
The RI Witness System: Technology for Traceability, Not Just AI
AI gets most of the attention, but one of the most practically important technologies in an IVF lab has nothing to do with artificial intelligence. What matters here is making sure your eggs, sperm, and embryos are correctly identified at every step.
How RFID-Based Witnessing Works
Systems like RI Witness use radio-frequency identification (RFID) tags to track and verify every sample movement in the lab. Each person is assigned a unique RFID identifier, and every time an embryologist handles a dish, the system confirms the correct match. If there is a mismatch, the system alerts the lab team and blocks the next step, while a complete audit trail documents who handled what, when, and in what sequence.
Why this matters as much as AI
RFID-based witnessing adds an extra layer of confidence to every step of the lab process. For anyone going through IVF, knowing that an electronic system independently verifies every handoff is a meaningful source of reassurance.
A clinic that pairs AI-assisted embryo grading with electronic witnessing for sample identification offers a more complete technology framework, and traceability is just as worth asking about as AI itself.
What AI in Fertility Treatment Changes about Your Odds, and What Still Depends on You and Your Doctor
Better embryo selection may help identify embryos with stronger development potential. Objective egg quality scoring may help set more informed expectations. RFID-based tracking may bring sample identification confidence to near-perfect levels.
Technology works best alongside the biological fundamentals that shape every cycle. Success in IVF draws on egg quality (which evolves with age), sperm health, uterine receptivity, and individual response to stimulation. A 2025 review in the Journal of Pharmacy and Bioallied Sciences (PMC) reinforced that AI is most effective when paired with clinical expertise and a thorough understanding of your biology.
AI models continue to improve as they train on increasingly diverse datasets from clinics worldwide, and researchers are actively working to make these models more interpretable. Clinics that are transparent about how their AI tools work and where the evidence stands are practising responsible fertility care.
Questions to Ask Any Clinic about Their AI Technology
Understanding how a clinic uses AI helps you make a more confident choice. Some questions worth raising:
- “Which specific AI tools does your lab use, and what are they certified for?” A clinic using a validated, certified tool like Vita Embryo or ERICA can name it. Specific answers are a good sign of genuine integration.
- “Does AI make the embryo selection decision, or does the embryologist?” In responsible practice, AI supports the embryologist rather than replacing them.
- “Do you use electronic witnessing for sample tracking?” RFID-based systems like RI Witness provide a verifiable safety layer and represent the current best practice in sample tracking.
- “Can I see the lab where my embryos will be cultured?” Clinics with a see-through embryology lab demonstrate transparency in how they operate.
- “How does your clinic personalise my treatment protocol?” AI can support personalisation, but it starts with how carefully your clinical team reviews your individual biology, history, and response patterns.
Asking these questions shows informed decision-making, and a clinic that welcomes them is usually one that has real answers.
Conclusion
When used alongside clinical expertise and proper lab infrastructure, AI in fertility treatment supports better-informed decisions at the moments that matter most.
At Luma fertility, we integrate AI-supported clinical workflows and RFID-based gamete tracking at every stage of treatment. Our see-through embryology lab and personalised care model are built around the belief that you deserve to understand exactly what is happening with your treatment. Our care specialists provide support at every stage, because fertility care is about the whole person.
Whether you are starting your journey or exploring next steps, get a free 15-minute clarity call with our team.
Note: The content in this blog is for educational and informational purposes only. Please consult a qualified fertility specialist for personalised medical advice.
Frequently Asked Questions
Q. How is AI used in IVF treatment?
AI in IVF analyses embryo images, assesses egg quality, and supports treatment personalisation. Algorithms trained on large datasets help embryologists grade embryos more consistently, adding objectivity to visual assessment. The fertility specialist always makes the final clinical decision.
Q. Does AI improve IVF success rates?
Current evidence suggests AI may support better embryo selection and treatment planning, and large-scale clinical trials are actively underway to measure its full contribution. Outcomes are shaped by biological factors like age, egg quality, and uterine health, which is why AI works best as part of a personalised care plan.
Q. What is Violet AI and how does it assess egg quality?
Violet is an AI-powered image analysis tool developed by Future Fertility. Trained on a large dataset of oocyte images from clinics globally, Violet non-invasively evaluates egg quality and predicts fertilisation potential, providing an objective score that offers more clarity than visual assessment alone.
Q. What is the RI Witness system in IVF?
RI Witness is an electronic witnessing system that uses RFID technology to track and verify every sample in the IVF lab. Each person is assigned a unique RFID tag, and the system checks that eggs, sperm, and embryos are correctly matched at every step. If a mismatch is detected, the system alerts staff and blocks the procedure.
Q. Is AI replacing embryologists in fertility clinics?
AI adds an objective data layer to the embryologist’s expertise. Embryologists bring years of clinical experience, contextual judgement, and hands-on laboratory skill that remain central to every decision, and AI enhances that process by ranking, scoring, and flagging patterns for their review.
Q. What questions should you ask a fertility clinic about their technology?
Ask which specific AI tools the clinic uses, whether they hold medical device certification, and whether AI or the embryologist makes the embryo selection decision. Ask about electronic witnessing for sample tracking, whether the lab is open for viewing, and how protocols are personalised to your biology. Clinics that welcome these questions typically have substantive answers.
Sources:
- World Health Organisation. (2023). 1 in 6 people globally affected by infertility. https://www.who.int/news/item/04-04-2023-1-in-6-people-globally-affected-by-infertility
- Salih, M., Austin, C., Warty, R.R. et al. (2023). Embryo selection through artificial intelligence versus embryologists: a systematic review. Human Reproduction Open. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC10426717/
- Mina, A., Younesi, M., Doohandeh, T. et al. (2025). Predicting pregnancy outcomes in IVF cycles: a systematic review and diagnostic meta-analysis of artificial intelligence in embryo assessment. Contraception and Reproductive Medicine. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC12476623
- More, A., Chimurkar, V., Mahajan, S. et al. (2025). The Integration of Artificial Intelligence in In Vitro Fertilization: A Comprehensive Narrative Review. Journal of Pharmacy and Bioallied Sciences. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC12156656
- Gao, Y., Yuan, Y., Wang, K. et al. (2025). Current progress and open challenges for applying artificial intelligence across the in vitro fertilization cycle. Patterns. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC12664965
