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The heart of the matter: How CordiFio is creating change
FemTech World speaks to Petronela Sandulache of CorDiFio about why heart condition diagnoses are harder for women
Heart health for women can be a difficult diagnosis to make due to symptoms mirroring other conditions. FemTech World meets Petronela Sandulache founder of CorDiFio
Petronela started her career in the consulting and automotive industry all over the world before a family tragedy made her assess her goals.
She said: “When something terrible happened in my family, it made me think about life and what it is all about. The most important person in our lives had a misdiagnosis and that was it. Someone who is relatively healthy just disappears overnight. That was when I started to investigate the disparities in medical diagnosis for women.”
Petronella added: “Women present different types of symptoms that get confused with indigestion or anxiety rather than being taken seriously and having a further investigation. Once I started investigating, I found out that the number one killer of women more than all cancers combined is heart disease yet 50 per cent following an attack get misdiagnosed.
This is huge pressure on the healthcare system because misdiagnosis means wrong treatments and doctors. The ones who are paying the price are women.”
Heart attacks and health
When it comes to heart attacks, women present different symptoms to men. Yet when it comes to diagnosis, the male symptoms are often used as a benchmark for how it presents.
Petronela explained: “In research conducted by the Canadian stroke and heart foundation, they discovered that 30 to 50 per cent of women diagnosed with depression are actually misdiagnosed and this can mask heart disease in women. They have only recently started to investigate the problem of heart disease, stroke or Alzheimer’s disease in women and why things present differently.”
Hormones are one reason why women have been excluded from studies and it’s why certain conditions present differently in women. Women also face challenges when it comes to taking part in clinical trials such as childcare or pregnancy.
“Obviously we have hormones that are not taken into consideration when you just study male models. So I thought we need to do something about this because femtech is just about tracking your period, making babies or menopause. Women are much more than their reproductive organs. Everything we know about common diseases, treatments and diagnosis has been done on men,” Petronela said.
“We need to do something about heart disease because if you don’t know that you have it, then you can be gone in five minutes. That’s why I called the company, CorDiFio, it means, in Italian, the heart of Fio which was the name of my mother. I’m dedicating this to her and all the women out there so they don’t need to go through the same type of thing,” she added.
The research also revealed that different ethnic backgrounds may place women at higher risks for cardiovascular diseases.
Petronela said: “It’s important to note within the female population, we have ethnic backgrounds with different risk profiles. We know that African, Hispanic or southeast Asian women have a higher probability of dying from cardiovascular disease compared to caucasian women.”
Heart health awareness
CorDioFio is dedicated to raising awareness so that women can detect heart problems early before it becomes a medical emergency. Its goal is to empower both women and their doctors to come up with early detection which will help to save lives.
“The great news is that 80 per cent of heart disease and strokes are preventable if caught in time. We are determined to keep women’s hearts beating longer.”
When it comes to CorDiFio’s technology, how does it work for patients?
Petronella explained: “If you download our app or go to the website to register then we take you on a journey by asking specific questions that take about 20 minutes to answer. We will generate a personalised health report for you to download and take to your appointment with your GP. You will learn your risk factors, where you need to keep an eye out and it’s something tangible to start that conversation with doctors by highlighting things you wouldn’t have thought of.”
She added: “We have tested this with various women from all over the world to see what their doctors say about the reports. They found that they don’t have a heart problem which is great so it’s peace of mind but some found they had other issues which present a diagnostic opportunity. We want to integrate this and are working with wearables.”
Australia’s drug advisory committee has recommended wider funding of triptorelin for women with breast cancer or endometriosis.
The recommendation comes after AstraZeneca announced plans to remove Zoladex from the market, risking leaving more than 7,500 women with breast cancer without an alternative treatment.
Both medicines block the release of oestrogen and testosterone and can be used as part of treatment, or for fertility preservation, in some forms of cancer.
The Pharmaceutical Benefits Advisory Committee met urgently in July and recommended making triptorelin unrestricted under the Pharmaceutical Benefits Scheme (PBS), which would mean it was funded for all uses.
The drug has been listed on the PBS for prostate cancer since 2006.
Triptorelin and Zoladex can also be used to treat endometriosis and to block puberty for either precocious puberty or gender-affirming care.
Vicki Durston, director of policy and advocacy at Breast Cancer Network Australia, described the recommendation as “a significant step forward” and said access to the medicine could mean the difference between life and death for some patients.
She said some women had already chosen to have their ovaries removed because of uncertainty over Zoladex supplies.
Marilla Druitt, Victorian state chair of the Royal Australian and New Zealand College of Obstetricians and Gynaecologists, said it remained unclear whether triptorelin would work exactly the same way as Zoladex, but the recommendation was likely to be positive for patients with endometriosis and pelvic pain.
She said: “I’m glad we’ve got an alternative.”
“That’s fantastic, and it remains to be seen whether or not it will be as good, but pain is so complex, pain is a really hard thing to study because it’s got so many contributors.”
Druitt said further research would be needed after the medicine was introduced.
If accepted by the federal government, the recommendation would also allow PBS funding of triptorelin for puberty suppression in precocious puberty and gender-affirming care.
This would make gender-affirming care federally funded through the PBS for the first time and would remove a financial barrier for transgender children in Queensland and the Northern Territory.
Stuart Aitken, medical director of Gender Health Australia, said the recommendation had sparked “absolute joy” among his patients.
He said: “It takes away a huge barrier to accessing evidence-based care.”
“It means that the ban has a very limited effect.”
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Sahar Abid is a Science Associate at Ema EQ, where she works on cultural sensitivity and bias in AI.
A woman asks an AI health assistant about postpartum depression.
She mentions that her in-laws are telling her to “push through” and skip medical help, even as her symptoms get harder to manage. She never says where she is from or names her background.
The assistant describes the condition and gives her a hotline number. It sounds correct, but it misses what she needs.
That gap is more common than the industry admits, and it points to a blind spot in how we test health AI for bias.
Most bias testing looks at what people explicitly say.
The typical way to check an AI for bias is to label a prompt with someone’s demographic details and see if the answer changes. That catches some problems but misses a bigger one.
Most people do not lead with their identity. They lead with their situation. The woman above told the assistant everything it needed to help her, just not in the form of a label.
Her real question was not only “what is postpartum depression?” It was “how do I get care when the people around me don’t want me to?
When family members hold sway over health decisions, and in many communities they do, advice that asks someone to overrule their family is not something they can act on.
The AI didn’t say anything factually wrong. It answered a different question than the one she was living.
We call this culturally implicit bias, meaning the AI misses the cultural context a situation implies rather than the context a person spells out.
When systems are trained to notice only the explicit cues, they fall back on a default answer built for the majority. For everyone else, the response can feel generic, off-target, or discouraging enough that they stop looking for help.
In health, that is not small. The people most likely to be missed are often the ones the system already underserves.
What we set out to test.
At Ema, we wanted to know how well AI picks up on cultural context that is implied but never stated. So we built our own way to test for it, across a range of communities and real situations like postpartum depression and fertility, using questions that carried cultural meaning without announcing it.
The patterns were consistent. Models often missed the meaning underneath the question. They dropped the specific details a person did share and smoothed them into something generic.
And even when they pointed toward real care, they tended to offer one option instead of choices that might actually fit a person’s life. Any one of those can be the difference between someone following the advice and walking away from care.
Why this matters for anyone building health AI.
Getting this right is the right thing to do, and it also works better.
When an answer reflects a person’s real context, people trust and act on the recommendations more, so they get the help and support they need.
Testing for it is harder than the shortcut most teams use. Swapping a name or a demographic label in and out is easy. Checking whether a model actually understands the human context around a question takes more care.
The shortcut teaches models to perform cultural competence instead of practicing it. No matter how much or how little someone chooses to share, they deserve an answer that is warm, complete, and usable.
A better question.
The bar for equitable health AI should be “does it serve someone who never told you who they are?” It is the harder test, but it determines whether real people get help.
The work of getting there is far from finished, and it is exactly what we are building toward at Ema.
Sources: Naidoo, V., & Chadha, K. K. (2025), Culturally responsive AI chatbots: from framework to field evidence, Computers in Human Behavior: Artificial Humans. Souligne, N., & Subbian, V. (2026), FairLogue: A toolkit for intersectional fairness analysis in clinical machine learning models.
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