How is AI taking reproductive medicine towards a more precise, efficient and personalised stage?

Artificial intelligence is no longer a distant promise in reproductive medicine. It is a reality that is changing the way oocytes are analysed, embryos are selected and clinical decisions are made in in vitro fertilisation laboratories. One of the most interesting advances in this area is related to egg donation, a field in which precision in oocyte allocation can make a huge difference, both in the efficiency of treatment and in the patient’s experience. 

For years, egg donation programmes have been based on relatively homogeneous models. In practice, this meant distributing oocytes among recipients according to general criteria, with the idea that all oocytes from the same donor had a similar potential. However, scientific evidence has gradually challenged this assumption. Even when they come from the same donor, not all oocytes have the same biological behaviour or the same capacity for embryonic development. 

This is where artificial intelligence comes in. Thanks to advanced image analysis and the use of predictive algorithms, it is now possible to estimate more accurately which oocytes are more likely to reach the blastocyst stage and, therefore, contribute to improving treatment outcomes. This opens the door to a profound change in egg donation. It is no longer just about distributing oocytes. It is about doing so with a smarter, more objective criterion that is much better adapted to each patient. 

 

From an empirical model to intelligent oocyte allocation 

Traditionally, egg donation has operated with a fairly uniform logic. The classic approach assumed that the number of oocytes assigned was one of the determining factors for success and that, within the same donation group, the potential quality was more or less comparable. This approach has allowed good results to be achieved for years, but it has also maintained a significant element of randomness in decision-making. 

The problem is that this model does not take one essential fact into account: biology is not uniform. Two oocytes from the same donor may behave differently in terms of embryonic development. One may have a high probability of reaching the blastocyst stage, while another may not. If both are treated as if they were equivalent, a very valuable clinical opportunity is lost. 

Artificial intelligence makes it possible to reduce this margin of uncertainty. Instead of assigning oocytes according to a general criterion, it allows a distribution based on their real developmental potential. This represents a major evolution within reproductive medicine, as it transforms an empirical strategy into one guided by objective data. 

 

A study that changes the way egg donation is understood 

One of the most relevant studies in this field was presented by IVIRMA Global at the 35th National Congress of the Spanish Fertility Society, held in Seville. The study, focused on the application of artificial intelligence in egg donation programmes, analysed more than 14,500 oocytes from 1,226 donation cycles. 

The main conclusion was very clear: it is possible to maintain high rates of embryonic development using fewer oocytes per patient, as long as the allocation is based on their real probability of development. 

This finding is especially important because it changes the classic approach to egg donation. Instead of focusing only on quantity, the concept of individually estimated oocyte quality begins to take centre stage. In clinical practice, this can mean greater efficiency, greater predictability and less emotional burden for recipients. 

 

ROSE, artificial intelligence applied to oocyte assessment 

The tool used in this study is called ROSE, and it represents one of the most interesting developments in current reproductive embryology. Its function is to analyse images of oocytes using artificial intelligence in order to detect patterns that the human eye cannot consistently identify. 

This has enormous value for several reasons. 

 

1. Objectiveassessment 

One of the major limitations of classic morphological analysis is that it depends, to some extent, on the professional’s experience and on criteria that may show a certain degree of variability. AI provides a layer of objectivity, as it works with data and mathematical patterns, not subjective visual impressions. 

 

2. Reproducibility

When an artificial intelligence system has been properly trained and validated, it can offer a much more reproducible assessment. This improves consistency between cases and reduces variability between operators or centres. 

 

3. Estimationof developmental potential 

ROSE does not simply indicate whether an oocyte “looks better” or “looks worse”. What it does is estimate its probability of reaching the blastocyst stage, in other words, of developing properly up to a key phase in assisted reproduction. 

 

4. Resourceoptimisation 

If you can identify which oocytes have the greatest potential, you can design a more efficient allocation strategy. This not only improves the clinical use of donated oocytes, but also allows each treatment to be better adapted. 

 

What advantages does AI bring to egg donation? 

The application of artificial intelligence in egg donation programmes is not simply a technical improvement. It has clinical, emotional and organisational implications. 

 

More precise assessment of oocyte quality 

The first major advantage is the possibility of assessing quality in a more refined way. This improves selection criteria and makes it possible to move from a more generalist strategy to a more personalised one. 

 

More efficient oocyte distribution 

Not all treatments require the same allocation logic. If the potential of each oocyte can be estimated, distribution becomes more rational and more effective. 

 

Adjustment of the number of oocytes assigned 

One of the most interesting changes is that the number of oocytes assigned can stop being set in such a homogeneous way and begin to be linked to their real biological potential. 

 

Greater clinical predictability 

Uncertainty is one of the heaviest burdens in assisted reproduction. The more the behaviour of biological material can be anticipated, the greater the team’s ability to design realistic strategies, and the easier it is for the patient to understand the process. 

 

Lower emotional burden 

When treatment is better adjusted from the beginning, part of the anxiety associated with the feeling of randomness is also reduced. And in fertility treatment, that matters a great deal. 

 

The impact is especially relevant in women over 40 

This advance is even more important in a specific group of patients: women over the age of 40 who undergo egg donation treatments. 

In these cases, precision in oocyte allocation can translate into very clear benefits: 

• a higher probability of achieving a favourable result from the first attempt  

• the possibility of obtaining additional viable embryos for future transfers  

• a reduction in the number of cycles required  

• less emotional and financial strain during treatment  

In addition, smarter allocation can help avoid the unnecessary generation of surplus embryos, which connects with a form of reproductive medicine that is more efficient, more personalised and also more sustainable from a clinical and ethical point of view. 

 

AI is already changing IVF laboratories 

It is important to make this clear: artificial intelligence applied to reproductive medicine is not a future hypothesis. It is already changing daily practice in many laboratories. 

Today, AI is being incorporated into areas such as: 

• embryo selection  

• oocyte assessment  

• sperm analysis  

• prediction of blastocyst development  

• personalisation of protocols  

This does not mean that technology will replace the embryologist or the specialist. What it does is add a layer of precision and support to decision-making. In other words, AI does not eliminate clinical judgement. It strengthens it with more information. 

And in the case of egg donation, this is especially valuable because it allows rigid models to be left behind and enables progress towards strategies based on probability,  eal biological quality and personalisation. 

 

From standardisation to personalised reproductive medicine 

The main underlying idea is this: reproductive medicine is moving away from overly standardised models and entering a stage in which each decision is increasingly adapted to the patient’s profile and to the real potential of the biological material. 

Egg donation is no exception to this change. For a long time, it has been managed with a reasonably uniform logic, but today the focus is no longer only on distributing resources, but on doing so better. 

This means: 

• less randomness  

• more objective data  

• greater strategic precision  

• greater clinical efficiency  

• a more personalised experience for the patient  

And this is probably one of the greatest advances that AI can offer in this field. 

 

Training in artificial intelligence applied to assisted reproduction 

All this technological change has a very clear consequence. Today’s and tomorrow’s reproductive medicine professionals need to understand these tools. 

It is not enough to know that they exist. It is necessary to understand: 

• how these algorithms are trained  

• what type of data they use  

• what limitations they have  

• how to interpret their results  

• how to integrate them into real clinical decisions  

Continuous training in artificial intelligence applied to assisted reproduction is therefore becoming one of the most strategic areas within advanced reproductive medicine. Not only for embryologists, but also for gynaecologists, geneticists, laboratory coordinators and professionals linked to fertility units. 

Because AI is no longer an eye-catching add-on. It is becoming an increasingly important part of the technical and clinical language of modern assisted reproduction. 

To conclude, artificial intelligence is driving a profound change in egg donation. Thanks to tools such as ROSE, it is now possible to analyse oocytes with greater precision, better estimate their probability of development and design allocation strategies that are much more effective than traditional models. 

This not only improves clinical efficiency. It also increases the predictability of results, optimises the use of donated oocytes, reduces uncertainty and promotes reproductive medicine that is more focused on each individual patient. 

Especially for women over 40, where egg donation often plays an important role, these advances can make a real difference in the probability of success and in the overall treatment experience. 

The direction is clear. Reproductive medicine is moving towards a model that is more precise, more personalised and less dependent on empirical decisions. And along this path, artificial intelligence is no longer a promise. It is a real tool that is redefining how laboratories work and how the fertility of the future is understood. 

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