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Don’t get lost – How femtech can navigate the EU medical device and AI rules

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By Xisca Borrás and Ellie Handy of the life sciences regulatory department at Bristows law firm

Femtech, short for female technology, is an important and fast growing sector. The EU is a key market for femtech, with five of the top 10 countries for femtech investment located in the EU.

Femtech products are developed for many areas of women’s health, such as menstrual health, pregnancy planning and monitoring, menopause and mental wellbeing.

As femtech is intrinsically linked to health needs, a key question for femtech products is whether they are regulated as medical devices or merely consumer products.

Additionally, many femtech products are embracing the use of artificial intelligence (“AI”). Therefore, another key question is whether products using AI will be regulated as “high-risk” AI systems under the EU’s new AI legal framework.

This article looks at when femtech apps and software qualify as medical devices in the EU and how the medical device and AI legal frameworks interact.

What is a software medical device?

The definition of “medical device” in the EU’s Medical Device Regulation 2017/745 (the “EU MDR”) includes software, used alone or in combination, that is intended by its legal manufacturer for a medical purpose. These medical purposes are listed in the EU MDR and include (amongst others):

  • diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of disease;
  • diagnosis, monitoring, treatment, alleviation of, or compensation for, an injury or disability; and
  • control or support of conception.

The legal manufacturer is the person that puts their name/branding on the device, and takes responsibility for it.

Whether software is considered a medical device will depend on whether the manufacturer states it has a medical purpose in the relevant documentation/materials.

The EU MDR defines intended purpose as “the use for which a device is intended according to the data supplied by the manufacturer on the label, in the instructions for use or in promotional or sales materials or statements and as specified by the manufacturer in the clinical evaluation”.

What is the test for qualifying as a medical device in the EU?

There is a selection of guidance documents that can assist you in determining whether a product should qualify as a medical device. We summarise some of the key guidance below:

  1. MDCG 2019-11 rev.1 

Under the EU MDR, the Medical Device Coordination Group (“MDCG”) has published guidance on the qualification and classification of software as a medical device. It sets out five decision steps to help determine if a piece of software is a medical device in the EU. The steps are:

  • Step 1: Is the product software?
  • Step 2: Is it standalone software (i.e., it is not an accessory nor driving/influencing the use of a hardware device) and does it not fall within Annex XVI?
  • Step 3: Is it performing an action on data beyond storage, archival, communication, simple search or lossless compression?
  • Step 4: Does it act for the benefit of an individual patient?
  • Step 5: Does it have a medical purpose (as set out in the medical device definition)?

If the answer to all five questions is yes, it will qualify as a medical device. In this case, manufacturers will have to ensure they comply with the pre-market requirements set out in the EU MDR before they can place the software medical device on the market.

Notably, they will need to set up a qualify management system, compile a technical file, undergo the appropriate conformity assessment and affix a CE mark.

Importantly, the manufacturers would also need to consider post-market requirements, such as having a post-market surveillance system and undertaking post-market vigilance.

3. Other relevant guidance

The MDCG has also published a manual on borderline and classification of medical devices under the EU MDR.

Additional sources of guidance may also be available from national competent authorities. The legal manufacturer could also look at examples of other products already on the market to see how they are regulated (e.g. looking at EUDAMED). Although, we would caution anyone relying too heavily on the regulation of other products as there is no guarantee they are compliant.

What if you’re not a medical device?

If the software does not qualify as a medical device, the product will not have to comply with the EU MDR.

However, the manufacturer should be careful about how it promotes its product and the claims it makes about it because, as discussed above, a medical device is defined based on the manufacturer’s intended purpose.

Let’s take the example of a mere period app. Using it for logging period dates, tracking ovulation, and predicting future cycles has no medical purpose and is therefore not a medical device.

However, if its manufacturer recommends this piece of software for contraception and/or to support conception it will suddenly have a medical purpose and so, it would qualify as a medical device.

As such, the manufacturer would either have to bring the device into conformity with the EU MDR or take action to change the promotional materials to remove the medical claims.

Interaction between medical devices and AI legal frameworks 

Under the EU MDR, devices are assigned risk classifications. For the lowest risk devices (Class I medical devices), the manufacturer can self-certify compliance with the EU MDR prior to the product being placed on the market or put into service in the EU.

However, high risk devices (Class IIa or above medical devices) must undergo a third party conformity assessment carried out by a notified body.

Notified body conformity assessments require a detailed review of the manufacturer’s quality management system, technical documentation, systems and procedures.

The process will often take more than a year to complete. Additionally, manufacturers have to grapple with ongoing burdens such as vigilance and post-market surveillance.

Under the EU MDR, most software as a medical device will be classified as a Class IIa or above.

Like the EU MDR, the EU’s Regulation (EU) 2024/1689 (the “AI Act”) also distinguishes between AI systems that pose different levels of risk.

The AI Act imposes onerous obligations on “high risk” AI systems, including in relation to accuracy, transparency, risk management, data quality and governance, and human oversight.

Although there is some overlap between the EU MDR and AI Act requirements, many are new AI-specific obligations. These pose a significant additional regulatory burden, increasing the complexity and cost of compliance for stakeholders.

Notably, the risk classification of an AI system that is itself, or is included in, a medical device is linked to the device’s classification under the EU MDR. Under the AI Act, AI systems are classified as “high risk” systems if:

(a) the AI system is a safety component of a medical device or the AI system itself is a medical device; and 
(b) the medical device is required to undergo a third-party conformity assessment under the EU MDR.

Therefore, low risk medical devices (i.e., Class I medical devices) that are self-certified cannot be “high risk” AI systems.

Whereas, any device that requires a notified body to perform its conformity assessment will be a “high risk” AI system, and so will be subject to the additional AI Act requirements.

Unfortunately for those wishing to avoid the “high risk” AI system requirements, there are relatively few Class I devices under the EU MDR.

Therefore, the majority of medical devices that are an AI system or have an AI system as a safety component will qualify as a “high risk” AI system.

One notable example of a Class I device is software intended to support conception by calculating the user’s fertility status based on a validated statistical algorithm.

If this kind of software medical device is also an AI system, it would not be classed as a “high risk” AI system, so it would not be subject to the more onerous requirements in the AI Act.

However, the manufacturers of these devices would need to carefully consider any product developments that add additional functionality, as this can impact the risk classification of the product under both the EU MDR and AI Act.

For example, if the manufacturer added functionality to the Class I device so it could also be used as a means of contraception, it would become a Class IIb medical device and would need a third party conformity assessment.

In turn, as the software is also an AI system, this would mean the AI system would be considered “high-risk” and be subject to additional regulatory requirements under the AI Act.

Whilst AI has the potential to provide tremendous benefits for femtech, it also triggers additional complexity that can be time-consuming and costly to navigate.

It is important to get it right in terms of compliance in order to maintain consumer trust, avoid regulatory penalties, and pave the way for long-term success and viability.

By Xisca Borrás, Partner – Life sciences regulatory and  Ellie Handy, Senior Associate – Life sciences regulatory at Bristows law firm.

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Diagnosis

AI may help accelerate breast cancer diagnosis for high-risk women – study

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AI may help speed breast cancer diagnosis for high-risk women after abnormal mammograms, a study suggests.

Women with abnormal mammograms often wait weeks to learn whether they have breast cancer.

Researchers at UC San Francisco and UC Berkeley said an AI-guided workflow could help reduce that wait by quickly identifying those most likely to have the disease. Some women could move from imaging to evaluation, and sometimes biopsy, in a single day.

Dr Maggie Chung, first author of the study, said: “This is a really an exciting time.

“This moves us closer to personalised care, where we can tailor a plan so that each patient gets the right intervention at the right time.”

The study used an open-source AI model called Mirai.

The model was trained on hundreds of thousands of mammograms linked to patients’ cancer outcomes.

A mammogram is an X-ray scan of the breast used to look for signs of cancer. A biopsy involves taking a small tissue sample to test for disease.

The AI tool is designed to detect subtle patterns in screening mammograms and predict a woman’s cancer risk.

Researchers at UC San Francisco and UC Berkeley applied the model to more than 4,100 screening mammograms at Zuckerberg San Francisco General Hospital and Trauma Center.

Mirai identified 525 women, about 12.7 per cent of screened patients, as high risk.

Those patients could receive an interpretation of their mammograms immediately after the scan and have additional diagnostic imaging for suspicious areas on the same day.

Some women who needed biopsies were also able to have them on the same day.

The researchers said Mirai reduced the wait time for diagnostic evaluation from several weeks to about an hour.

For women who were ultimately diagnosed with breast cancer, it reduced the average wait for biopsy from more than two months to fewer than 10 days.

The researchers stressed that Mirai does not replace radiologists or make diagnoses on its own.

Instead, it acts as a triage tool to help physicians identify the patients who can benefit most from accelerated care.

The team analysed more than 114,000 archival mammograms before launching the programme, to ensure the model would capture enough high-risk patients without overloading the clinic with too many expedited evaluations.

The researchers said they hope AI will support a more personalised approach to breast cancer screening tailored to each patient’s breast cancer risk.

Chung said: “Right now, many women follow the same screening schedule but their individual risk can be very different.

“AI risk assessment gives us the chance to identify the women most likely to benefit from expedited care and get them what they need.”

Adam Yala, senior author of the study and a data scientist at UC Berkeley, said: “This is a powerful example of how AI can be a collaborative partner for physicians.

“It shows how we can improve care when we bring clinicians and data scientists together to design these systems.”

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Pregnancy

Type 2 diabetes raising twice as fast in younger womem, research finds

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Type 2 diabetes diagnoses are rising twice as fast in women under 40 as in women over 40, new data shows.

Type 2 diabetes is a serious condition and can lead to complications such as heart attacks and strokes. When it develops in younger people, it can be more aggressive and have more severe and acute effects.

Diagnoses in women under 40 rose by 47 per cent between 2017/18 and 2023/24. By comparison, diagnoses rose by 22 per cent in women aged 40 to 79.

During the same period, type 2 diabetes diagnoses in men under 40 increased by 34 per cent.

Diabetes UK said it is concerned about the follow-up care offered to women who have had gestational diabetes, also known as GDM, which increases the risk of developing type 2 diabetes after pregnancy.

Gestational diabetes is high blood sugar that develops during pregnancy and usually goes away after birth, but it raises the risk of type 2 diabetes later.

Colette Marshall, chief executive at Diabetes UK, said: “These figures should be a wake-up call. Type 2 diabetes is rising twice as fast in younger women compared to older women, and a crucial opportunity for prevention is being missed. Every diagnosis is life-changing, but when it develops in younger people, type 2 diabetes is even more aggressive.

“Pregnancy shouldn’t be a pathway to ill health. Yet despite facing a much higher risk of type 2 diabetes, too many women with GDM receive little or no follow-up care after pregnancy.

“As the Government turns its Strategy into action, support for women who have had gestational diabetes must not be overlooked.”

Last year, the NHS published the first national GDM audit for England in 2024/25, which revealed inconsistencies in follow-up care.

Only 57 per cent of women with GDM received an annual HbA1c test, which should be offered to every woman with GDM.

An HbA1c test measures average blood sugar levels over the previous two to three months.

Only 4.5 per cent of women had received support through the NHS Diabetes Prevention Programme.

The report also found that 11 per cent of women developed prediabetes within five years of having GDM, while 15 per cent developed type 2 diabetes within 10 years.

Prediabetes means blood sugar levels are higher than normal and a person has a higher risk of developing type 2 diabetes.

A recent survey funded by Diabetes UK also found that more than a third of women with GDM felt abandoned by healthcare services after giving birth.

If you live in England and have had gestational diabetes, you can self-refer to the NHS Diabetes Prevention Programme, which supports people at risk of developing type 2 diabetes. If you live in Northern Ireland, Scotland or Wales, you can speak to your GP about support.

Diabetes UK has written to women’s health minister Baroness Merron calling for urgent improvements to postnatal support for those diagnosed with GDM during pregnancy.

GDM affects between 10 and 20 per cent of pregnant women, but Diabetes UK said cases have long been underreported and UK-wide data on the condition has not been readily available.

The charity said poor follow-up care for women who have had GDM may be contributing to rising rates of type 2 diabetes in younger women.

It is calling for consistent postnatal follow-ups for women after GDM, more referrals to the NHS Diabetes Prevention Programme, greater accountability for improvements in postnatal care, and action on inequalities affecting women from deprived and minority ethnic communities.

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Diagnosis

Millions of women with breast cancer could be spared chemo with genomic test

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A genomic test may help some women with breast cancer avoid chemotherapy, with near-identical outcomes in an international trial.

The findings suggest patients with a low test score could be treated with hormone therapy alone without increasing the risk of their cancer returning.

Researchers said the results could support more personalised treatment decisions and spare some women the side-effects of chemotherapy.

Prof Rob Stein, the trial’s chief investigator and a professor of breast oncology at UCL, said: “Optima addresses a longstanding challenge in breast cancer care: identifying who truly benefits from chemotherapy and who does not.

“Our findings show that many patients can safely avoid chemotherapy without compromising their outcomes.

“These results mark an important and significant step toward more personalised treatment.

“The trial has successfully used tumour biology to guide decisions rather than relying solely on traditional clinical features.”

Breast cancer treatment usually involves surgery to remove tumours. Chemotherapy is then often recommended if doctors believe there is a risk the disease will return.

Chemotherapy can cause side-effects including hair loss, rashes, nausea, insomnia and fatigue. Some women may also face longer-term consequences such as infertility, cognitive impairment or early menopause.

The Optima trial followed more than 4,000 patients with newly diagnosed breast cancer in the UK, Norway, Sweden, Australia, New Zealand and Thailand.

The trial was led by University College London.

One woman who took part in the trial told the Guardian that being able to skip chemotherapy felt “like Christmas”. Nine years after being diagnosed, taking the test and skipping chemotherapy, she is healthy and enjoying a full and active life.

The trial tested whether a genomic test could identify which patients need chemotherapy and which could safely avoid it.

The Prosigna test, made by diagnostics company Veracyte, analyses the activity of 50 genes in tumour tissue. It identifies the molecular subtype of the cancer and gives a score estimating the risk of breast cancer returning in the next 10 years.

The randomised trial involved 4,429 patients aged 40 or over with hormone-positive breast cancer. Hormone-positive breast cancer grows in response to hormones such as oestrogen or progesterone. It is the most common form of breast cancer, accounting for up to 80 per cent of cases globally.

Participants were assigned to one of two groups. In the standard treatment group, patients received chemotherapy followed by hormone therapy.

In the second group, patients had their tumours analysed using the genomic test. Those with a high score received chemotherapy and hormone therapy. Those with a low score received hormone therapy alone.

Radiotherapy and other treatments were given as usual in both groups.

In the second group, outcomes were very similar whether chemotherapy was given or not. Five years after treatment, 95 per cent of patients who had chemotherapy and hormone therapy were alive and free from breast cancer recurrence, while 94 per cent of those who skipped chemotherapy were also alive and recurrence-free.

The findings suggest chemotherapy offered little or no additional benefit for patients with low test scores.

Some men also took part in the study, but researchers said there were too few to draw firm conclusions for this group.

The trial received funding from the National Institute for Health and Care Research, Veracyte and cancer charities.

Prof Iain MacPherson, a co-chief investigator and professor of breast oncology at the University of Glasgow, said: “Optima provides robust, practice-changing evidence that we can safely reduce the use of chemotherapy for many patients with hormone-sensitive breast cancer.

“These findings represent a major step forward in delivering more personalised, precise care, ensuring that treatment decisions are driven by what will genuinely improve outcomes for patients, while avoiding unnecessary toxicity.

“The potential impact for both patients and health services is substantial.”

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