The role of artificial intelligence in fertility treatment

At the recent Fertility Show Africa 2022, Dr Mandy Rodrigues – resident clinical psychologist at Medfem Fertility Clinic – introduced the Fertility Patients Care Guidance, published by the European Fertility Society (EFS), which is now being used to improve patient care in fertility clinics worldwide.

Our Medfem team is very proud of Dr Rodrigues’ role in the European Fertility Society and her contribution to this important Guide that will benefit fertility clinics globally. We gladly share some insights from this Guide with regard to the role of artificial intelligence (AI) in assisted reproductive therapy (ART) and what it means for fertility patients.

Published globally earlier this year and introduced in South Africa at the recent Fertility Show Africa 2022, the European Fertility Society’s (EFS) Fertility Patients Care Guidance provides an insightful look at the role that artificial intelligence plays in modern fertility treatments, which we share in this article. (You can download a pocket version of the Fertility Patients Care Guidance from

In the Guide, the EFS notes that artificial intelligence (AI) solutions are the future of assisted reproductive therapy ART, and paving the way to this future are ‘big data’ analytics solutions that already exist.

The main advantage of big data analytics is the ability to accurately predict success rates for individual patients, based on specific circumstances. Specialised technology platforms that use these big data solutions are already operational or under development. These platforms use databases containing thousands of patient profiles with very detailed information on medical history and fertility examinations, as well as outcome data. This data is used to find similarities and draw conclusions regarding potential patient outcomes, and to estimate individual success rates.

Building on big data analytics, AI can further enhance technology platforms that estimate success rates of ART such as IVF (in vitro fertilisation). While AI systems are still at the research and development stage, ‘machine learning’ and the accurate identification and interpretation of data patterns by AI systems can reach conclusions potentially beyond human capability.


AI contributes to better outcomes in ART by not only estimating success rates, but also by recommending treatments, methods and protocols – and even the best clinics or locations – for optimal chances of success.

AI can impact three main areas of fertility treatment:

1. estimating outcome percentages more accurately;

2. enabling better patient service, selecting appropriate patient protocols, and evaluating suitable types of treatment and lab methods to optimise results; and

3. assessing cost-effectiveness of fertility treatments and reducing unnecessary expenses from failed cycles.

How AI is used in ART

Because of the vast array of causes of infertility and the many treatment options, combined with a lack of automation, there can be considerable differences between the diagnoses and treatment protocols provided by various specialists or clinics.

These differences can be reduced through AI: learning from vast amounts of clinical, demographic, pathological, imaging and laboratory data, and making connections and recommendations to guide healthcare decisions. In addition, automating and streamlining the entire process should reduce the overhead costs for fertility practices and improve access to treatment for many patients.

For this reason, AI applications can be found in various areas of ART, notably in triage, screening, and diagnosis; the prediction of outcomes; the personalisation and monitoring of treatment; and image interpretation. Each of these are briefly detailed below.

AI in triage, screening and diagnosis

In the context of determining treatment priority or urgency (triage), screening, and diagnostics, AI is used to efficiently interpret large health datasets.

The AI systems are trained on external health data that have usually been interpreted by humans and that have been minimally processed, for example, clinical images that have been labelled and interpreted by a human expert. The AI system learns to execute the interpretation task on new health data of the same type to make a diagnosis.

It is particularly useful to identify patients who need fertility treatment more urgently, and could even be used to diagnose specific conditions such as endometriosis, polycystic ovary syndrome (PCOS) or diminished ovarian reserve.

AI in the prediction of outcomes

Effectively predicting treatment outcomes and estimating success in IVF is a complicated task.

Traditionally, success rates are estimated by medical experts based on the patient’s medical history and the findings of the medical examination, as well as on the expert’s experience. Unfortunately, this traditional approach is vulnerable to human error.

Current technology developments are providing important tools to eliminate human error and miscalculations, and can provide accurate estimated success rates easily and quickly.

These solutions are widely available and constitute the first step in showing what technology can offer in terms of accurately estimating IVF success rates.

AI in the personalisation and monitoring of treatment

Different patients respond differently to various fertility drugs and treatments.

AI and machine learning can be used to automate the complex task of analysing all the individual factors, to compare the outcome against a benchmark and to make personalised treatment recommendations.

Similar algorithms can be used to monitor the patient’s response during treatment, by assessing, for example, the risk of ovarian hyperstimulation syndrome (OHSS) or poor ovarian response.

AI in imaging interpretation

Imaging interpretation is potentially the most advanced application of AI in ART.

Obstetric and gynaecological ultrasound scans are two of the most widely performed imaging studies in ART and there is huge potential for AI to provide quality assurance in imaging interpretation.

For example, embryo selection for transfer has historically been based on human experience and expertise. AI offers a system that learns from embryo development patterns and the implantation success of those embryos and can effectively grade embryos according to real success potentials to eliminate human error and standardise the embryo selection process. Through ‘machine learning’, the AI improves continually, providing ever-more accurate predictions over time.

The application of AI solutions is available not only at the embryo selection stage, but also one step earlier: at the selection of eggs and sperm. Research is currently underway and solutions are being tested on how to select the best available sperm using AI algorithms. This is particularly important in cases of sperm samples with low normal morphology, high fragmentation and altered DNA.

Can AI assist in your fertility treatment?

While AI is currently being applied in several areas to improve the decisions made by fertility experts, it doesn’t – and never will – replace the expertise and experience of qualified and caring fertility specialists and embryologists.

Nevertheless, ongoing research and development will ensure AI systems become even more proficient in assisting fertility experts to reduce human error and improve success rates, as well as generating more affordable, more accessible and quicker results.

Our team at Medfem Fertility Clinic believes in making world-class fertility treatments available for everyone. It is our joy and commitment to give you a positive outcome to your fertility journey, so you will have a fond memory of feeling empathy, caring and being part of the Medfem Fertility Clinic family.

If you would like to meet one of our fertility specialists at Medfem Fertility Clinic, simply click here to book an initial consultation or contact us telephonically on +27 (11) 463 2244.

Our Fertility Specialists can also meet with You During a Virtual Consultation Via Zoom or Skype. Click here to book a virtual consultation now.
We look forward to meeting you!

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