The Gender Data Health Gap:
Harnessing AI’s Transformative Powerto Bridge the Gender Health Data Divide
FemTechnology | Women At The Table
November 2023

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The data we need for women’s healthin the 21st century is missing.
The digital age promises unparalleled advancements in healthcare
through AI, but we stand at a crossroads. As we chart our path forward,
we must confront the glaring void in women’s health data. How can westeer AI’s prodigious capabilities to not only recognize but also
address this deficit? And more crucially, as we’re on the brink of an
AI-driven healthcare revolution, how can we redesign this future to be
inclusive, ensuring women’s health isn’t relegated to the periphery?
The Problem
The foundation of contemporary women’s health data is fractured:● Historical Exclusion: Until 1993, women were not mandatorily included as
subjects in clinical research in the US.1 This startling omission spans
critical areas: prescription drugs, vaccines, cancer research, cardiovascular
health, mental well-being, Alzheimer’s, and more.
● The Impact of Time: Even though women have been included for the last 30
years, there exists a lag of about 17 years from translational research todirect patient care.2 Effectively, this means our actionable data on women’s
health is barely in its adolescence.
● Ongoing Biases: Fast forward to 2023, and a majority of biomedical
research still relies predominantly on male mice,3 perpetuating the cycle ofgender-skewed data.

The Implications
The repercussions are profound:1

Women’s involvement in clinical trials: historical perspective and future implications - PMC
The answer is 17 years, what is the question: understanding time lags in translational research - PMC
3
Twenty years and still counting: including women as participants and studying sex and gender inbiomedical research - PMC
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● Medicine’s Gender Skew: The diagnostic and treatment paradigms inplace today largely reflect male-centric data, leading to potential
misdiagnoses, suboptimal treatments, and inequitable health outcomesfor women.4● AI’s Magnifying Effect: Existing AI models in healthcare are trained onthese limited datasets. The swift adoption and reliance on AI, without
rectifying these foundational gaps, threatens to perpetuate and even
amplify these disparities at an unprecedented scale.
● Global Ramifications: With 90% of US hospitals marching forward with
AI-centric strategies5, the reverberations of these gaps aren’t just national;
they’re global.6 Part of this problem is structural, some is systemic, all willbe amplified by the use of AI as we currently practise it.7

What is the Gender Data Health Gap?
The Gender Data Health Gap is the difference in quality andquantity of health data both collected and analyzed between
women and men.

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Sex bias exists in basic science and translational surgical research
90% of Hospitals Have Artificial Intelligence Strategies in Place
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Closing the gender data gap in healthcare | McKinsey
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Raising awareness of sex and gender bias in artificial intelligence and health - PMC
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Why does the Gender Data Health Gap matter?
The Gender Data Health Gap (whether conscious or unconscious)
is often a factor perpetuating the disparities that women andmen experience in healthcare provision.
This can result in:● Delays in diagnosis:
Women wait an average of 4 years longer to receive a diagnosis for thesame disease as men.8
Many of the ways in which we diagnose diseases (e.g. ‘cut-off’ values,
symptoms or even the instruments used) are overly reliant on the whitemale model of presenting. This results in women being underdiagnosed for
diseases that are present in both sexes but manifest differently. 9● Not being prescribed the appropriate treatment:
In a cohort study of adults with acute nontraumatic abdominal pain,
women were 13% – 25% less likely to receive opioids in the emergency room
for their pain despite presenting with the same pain scores.10
Conversely, between January 2017 and December 2021, women in England
were 59% more likely to be prescribed benzodiazepines (medication often
used to combat anxiety and insomnia) 11 - better known by the brand namesof Valium, Xanax and Temazepam - than men. In 2020, the FDA mandated
that a “black box warning” be placed on benzodiazepines to inform patientsthat withdrawal from the drugs can be life-threatening.

8

Population-wide analysis of differences in disease progression patterns in men and women | Nature Communications
Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identificationand treatment of attention-deficit/ hyperactivity disorder in girls and women
10
Gender disparity in analgesic treatment of emergency department patients with acute abdominal pain
11
Hundreds of thousands more women than men prescribed powerful anti-anxiety drugs 'harder to come off than heroin'
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● Care that is not structured to take differences in account:
In a high volume cancer centre:

9 out of 10 men
Undergoing brachytheraypy forprostate cancer were asked
about their sexual healthvs 1 out of 10 women

Sexual function was
discussed with

89% of men
vs 13% of women

Having brachytherapy forcervical cancer

At their initial radiation therapy
consultation

81% of men

Compared toWith prostate cancer had theirsexual health assessed using apatient-reported outcome tool

0% of
women

Some research has indicated that women athletes are more susceptible tomuscle and tendon injury during ovulation12. Female athletes may also be more
susceptible to having concurrent issues like eating disorders, multiple stressfractures, gastrointestinal issues and mental health concerns. But the approachto these issues (although they are related) is fragmented. An interdisciplinary
approach to these issues is not yet the norm.

12

Injury Incidence Across the Menstrual Cycle in International Footballers

5Which parts of the healthcare ecosystem, and who is
impacted by the Gender Data Health Gap?
The Gender Data Health Gap is embedded in the workflow of eachand every stakeholder in the healthcare ecosystem, whether they
are conscious of it or not.

1. Physicians● Have a lack of training on how diseases may present differently inwomen or conditions that only impact women.
41% of UK universities do not have mandatory menopause education on the
curriculum.13 This situation results in: 1 in 3 women between 45 to 54
being given an incorrect diagnosis before finding out their symptoms are
related to menopause, according to a study that also revealed 32% of
women feeling their doctor was not very knowledgeable about the topic14.
● Structural bias in diagnostic tools.
○ Heart Attack:■ Cardiac troponin (cTn) test: Is used to measure the level of troponin(protein released by damaged heart muscle) in the blood. Higher
levels of troponin are used as an evaluation parameter for more heartdamage. The clinical threshold that signals a heart attack can differ
between men and women, i.e. a woman could be having a heart
attack but the troponin level would be below the level of detection.
■ Cardiac catheterization: is used to detect blockages in large arteries.
Women are more likely than men to have plaque buildup in thesmallest arteries due to inflammation, which could be better
visualized with an MRI.
If the tools used for screening for heart attacks render ‘invisible’ the heart
attacks women have, this can further contribute to misconceptions about
what type of individual has a heart attack. This is a part of a reason (but not
the only one) that women have a 50% greater chance of misdiagnosis of aheart attack compared to men.

13
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Menopause knowledge and education in women under 40: Results from an online survey - PMC
Nearly 1 in 3 Women Have Had Their Menopause Symptoms Misdiagnosed

6● Lack of treatment options to prescribe to patients.
There are only 2 FDA-approved treatments for female sexual dysfunction which impacts approximately 40% of women in the world - vs 27 treatment
options for men.15
The first treatment option for sexual dysfunction for women was only
approved in 2015 vs men’s treatment options beginning in 1998.
Furthermore, research investigating the interaction between alcohol andAddyi, one of the drugs developed for treating female sexual dysfunction,
included 23 male participants and only 2 female participants.
● Patient dismissal.
Variability in national regulatory decision:
Medicalabortion pill

Oral contraceptive pill(birth control)

Sildenafil (“viagra”) for
erectile disfunction

Time for approval by
USA FDA:Time for approval by
Ministry of Health, Japan:

Time for approval,
Japan & USA

4 years

35 years

6 months

84% of womenreport feeling dismissedby their GP in the UK.16

Nearly 1 in 4 women

say they do not feel their clinician
takes their pain seriously(versus 1 in 6 men). 17

2. Physicians● Lack of awareness around female-centric diseases, which are diseases
that disproportionately impact women or diseases that present differently
in women result in:○ Delays in diagnosis (from the patient side) as women are not awarethat they should seek care, or that care is indeed available.
15

A discussion about treatment options for women's sexual dysfunction
Results of the ‘Women’s Health – Let’s talk about it’ survey - GOV.UK
17
See My Pain | Nurofen
16

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An incomplete picture of potential complications of the condition. For
example, the link between PCOS (Polycystic Ovary Syndrome), which impacts anestimated 8-13% of reproductive aged women and cardiovascular disease studies
suggest that women with PCOS have 2x risk of a future cardiovascular event, suchas a heart attack or stroke.

3. Researchers● Lack of sex disaggregated data.
In COVID-19 vaccine trials, 28.3% of publications did not report sex
distribution among participants. Only 8.8% of the studies provided sexdisaggregated Vaccine Effectiveness estimates.18
Sex-disaggregated data is essential to understand whether there are
increased side-effects in one sex or the other, and more generally to
18

Sex-disaggregated effectiveness data reporting in COVID-19 vaccine research: a systematic review - PMC

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understand the distributions of risk, infection and disease in the
population. For example, a study that looks at the impact of a new drug on
pain relief might draw the overall conclusion that a drug is effective forboth men and women.
Disaggregation would allow visibility into whether the drug works
better in one sex than the other, as women and men may well have different
mechanisms for experiencing pain19. Data disaggregated by sex also allows
better visibility into how resources are allocated.
● Non-representative numbers of females and males in studies.
A 2010 survey examining 2000 animal studies found that 80%
included more males than females. As recently as 2016, 70% of biomedical
experiments did not include sex as a biological variable, and of those that
did include sex as a biological variable less than half of them included bothmales and females in their trials.
Women’s participation in clinical trials remains low, especially inphase I trials (around 22%). Pregnant and lactating women continue to be
excluded from clinical trials – even in postmarket phases. This lack ofevidence poses challenges for physicians when advising pregnant andbreastfeeding women in need of medical treatment.20-

Meaningful representation and analysis of outcomes by sex when it
comes to clinical trials still falls short – for instance, the approval of
emtricitabine/tenofovir alafenamide (Descovy) for HIV pre-exposure
prophylaxis in the United States in 2019 was limited to men andtransgender women, excluding individuals assigned female at birth. The
manufacturer cited difficulties in recruitment, resource constraints, anduncertain expectations of achieving significant clinical outcomes as thereasons for this exclusion.

Current guidelines suggest that women should be included in trials
in proportion to their prevalence in specific health conditions, but thistarget is not met in serious disease areas like cardiovascular conditions
and certain cancers. Out of the 40 medicines approved by the FDA in 2019
for conditions affecting both sexes, 16 had less than 50% representation of
women.

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Why the sexes don’t feel pain the same way
Gender bias in research: how does it affect evidence based medicine? - PMC

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Focusing solely on prevalence fails to address sex differences in diseaseprogression or mortality. For example, in the case of erdafitinib, which
the FDA approved for a type of bladder cancer in 2019, only 21% of theparticipants were women. The justification for this skewed ratio wasthat men were affected at a significantly higher rate, even though
women had poorer prognostic and survival outcomes, even withalternative treatments.

● Not having visibility into where the gaps in women’s health researchexist
As an example, the NIH has not assigned a unique identifier code tomenopause, unlike other conditions such as anorexia or prostate cancer.
This means that anyone who wants to know how much funding the NIH hasawarded for research on menopause must manually count the number ofgrants that mention “menopause” in their titles or descriptions.

4. FemTech Startups● Gender Data Health Gap
Lack of consistent data surrounding prevalence, incidence, economic andquality of life burden of diseases in women’s health impacts the ability of many
FemTech Startups to craft more traditionally compelling businesses-cases.
Without data the problem is often dismissed as too ‘niche’ or ‘small’ to offer
solutions for their populations.
Lack of data also poses a problem from an investment perspective. It makes it
difficult for startups to objectively quantify the specific problem they are solving,forced to rely instead on more general metrics such as the size of the ‘FemTech’sector as a whole.
Furthermore, data often acts as a substitute for lived experience - which, in the
FemTech sector, is vital, as those in decision making positions often have notexperienced the problem themselves. For example, startups that tackle fertility
and cancer treatment within FemTech are the ones that routinely raise the mostmoney (these are also areas that male founders in FemTech are more involved in).
70% of femtech startups are founded by women, yet male-founded startups
raise more capital. On average, female-founded femtech startups raise $4.6m,
compared to $9.2m by those with all-male teams.21
FemTech startups thus face an uphill battle, having to convince
stakeholders of the need for their solution in the first place. If they are21

https://sifted.eu/articles/even-in-femtech-it-still-pays-to-be-a-male-founder10

successful in convincing stakeholders of the need and market opportunity for
their solution, they are then confronted by the challenge of a lack of pre-existingdata sets from which to base their solutions. It is difficult to develop diagnostic
and/or therapeutic solutions for women’s health conditions when much of thefoundational data and basic translational science in the field has not yet been
carried out.
As an example, we still have a fundamental lack of research and understandingof the pathophysiology of many ‘common’ women’s health conditions such as
Endometriosis (which impacts approx. 1 in 10 women, Uterine fibroids (which up to
77% of women during childbearing years experience), PCOS (which 1 in 10 womenexperience) or PMDD (which impacts between 1-12 and 1 in 20 women ofchildbearing age). Subsequently, all of the aforementioned conditions rely on
imprecise diagnostic tools and none possess a cure.
● Novel ways to collect data sets to tackle the Gender Data Health Gap inreal time
As we have noted, women’s health is rife with information that has not been
researched and data that have not yet been collected. FemTech start ups are
uniquely poised to disrupt this by collecting novel women’s health data sets inreal time.
● Patient Reported Outcomes
What is it that women want to know about their own health? Where
are they looking for answers and consistently not finding solutions? How
can we prioritise the problems women themselves want to be solved inorder to identify the most pressing ‘unmet needs’.
-Roche’s #MyStoryForChange initiative: The stories of 600 womenacross the globe were collected to better understand the interaction
bias women experience with the medical system. A theme thatemerged in healthcare settings is that many women feel as if they
are not being listened to or that their experiences are not being taken
seriously.
There is a need to find alternative ways to access the healthcare

experiences of women, to really learn what issues women are struggling
with. We need qualitative as well as quantitative data.
-

Clue, the #1 doctor-recommended free period tracker app built incollaboration with top health researchers, is a good case study in thevirtuous cycle of women being able to use their own data to advocate
11for themselves. In the words of Audrey Tsang, Co-Ceo, Clue: “We often
hear users in our community say that ‘I just want to be taken
seriously’. The world today doesn’t take their pain or their concernsthat ‘something doesn’t feel right’ seriously. That’s why they track in
Clue—so that their data can help them advocate for themselves andthe care they need”● Reimagining care
Women are more likely to suffer from chronic conditions yet ourcurrent healthcare model is structured to service acute situations (and is
episodic in how it engages patients).
If we employ a more consistent, longitudinal means of engaging
patients that would expand the type of information we are able to collect.
For example, noticing patterns in depressive episodes that might be linked
to hormonal fluctuations.
● Collecting novel biomarkers and datasets via FemTech Startups:
FemTech startups are uniquely poised to collect data sets that havebeen previously neglected. Some participants in the 2023 FemTechnology
Gender Data Health Gap Workshop doing this are:
○ Impli: Continuous hormone monitoring via an implantable device,
which has the potential to assist fertility specialists in selecting thebest treatment by understanding the patient's hormonal profile, or
improve chances of successful fertilization and implantation by
optimizing medication dosages and timing.
○ Daye: At-home vaginal microbiome screening, using a diagnostic
tampon to check for infection-causing microbes and fungi, as well as
levels of protective, good bacteria.
○ TheBlood: Analyzing menstrual blood for unique biomarkers in women’s
health via an at home blood analysis kit for menstrual blood.
○ Sanno: Supporting patients with chronic conditions in gut andmetabolic health, immunology and women’s health to connect themwith clinical trials in the space.

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The gender data gap is turbocharged by AI
In the context of continuing and widespread AI adoption in healthcare, we
run the serious risk of structurally embedding biases and gaps. Without being
aware. Again.
AI learns from the data it’s trained on therefore if women are ‘invisible’ ormisrepresented in that data, this can have lethal consequences.

Gender bias revealed in AI tools screening for liver disease22.

What is algorithmic bias in a healthcare context?
Defined for the first time in 2019 in the Journal of Global Health as “theinstances when the application of an algorithm compounds existing inequities insocioeconomic status, race, ethnic background, religion, gender, disability orsexual orientation to amplify them and adversely impact inequities in healthsystems.”23
We are becoming more and more aware of how this nexus of missing data sets
and our speed in deploying AI models with missing data will entrench inequities.

22
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Gender bias revealed in AI tools screening for liver disease | UCL News - UCL – University College London
Artificial intelligence and algorithmic bias: implications for health systems - PMC

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How does bias enter into AI in health research?
Table: Examples of Types of Bias in Artificial Intelligence & their descriptions24
Type of Bias

Description

Inherent or
Historical Bias

Even when data is accurately collected and sampled, models
might yield undesired results due to pre-existing societal
biases in the world.
e.g. making the mistake of associating HIV primarily with gay andbisexual men because of its higher occurrence in this group.

Representation or
Sampling Bias

When certain segments of the data input are not adequately
represented.
e.g. a large part of genomics research predominantly focuses on
European male demographics, sidelining other ethnic groups.

Data Proxy Bias

When the data we collect serves as an indirect measure for thedesired attributes.
e.g. using various clinical, social, and cognitive indicators to identify
early stages of schizophrenia, even though gender differences caninfluence the manifestation of these indicators and their related
psychosis risk.

Generalization /Aggregation Bias

When a universal model is applied to groups that havedistinct underlying conditions.
e.g. despite diabetes’ variable interpretations across different
ethnicities and sexes. the widespread use of haemoglobin A1c (HbA1c)
levels to diagnose and track the disease.

Evaluation or
Benchmarking Bias

when the data used to test or benchmark an algorithm isn't agood match for the intended audience.
e.g. the underwhelming performance of certain facial recognition
technologies on individuals with darker skin tones, especiallyfemales, because most benchmark images are sourced from whitemales.

Modelling Bias

Bias can be inadvertently or deliberately embedded into analgorithm, especially when relying on improvised solutions.
e.g., when a commercial health prediction algorithm used healthcare
costs as an indicator for health condition without factoring inprevalent disparities in healthcare accessibility, it displayedconsiderable racial prejudice changing predictions of genuine need.

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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare - PMC

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Synthetic Data
As we wait to collect the much needed missing data sets on women’s healththere is a discussion about the use of synthetic data to augment data sets. Thisis a very promising approach on a number of levels but which has its own
limitations if not thought through carefully.
Before using synthetic data to add to a dataset it must be noted that many
patient cohorts had minimal participation in the original data.25 Statistics showthat racial and ethnic minorities comprise 39% of the United States
population but only account for 2% to 16% of clinical trial participants.
Factors

like

age,

biologicalsex, disabilities, chronic comorbidities,

geographical location, gender identity, race, and ethnic background may influence
how an individual reacts to a certain drug, medical device, or treatment plan. If
patients in clinical trials do not represent the whole community, there is the riskthat differences in drug metabolism, side effect profiles, and outcomes will bemissed.
This also translates when using synthetic data. The lack of diversity insynthetic patient cohorts can result in AI models that perform poorly on real-world
populations. As an example, generating data for 500 Black male patients and 500
Black female patients using a synthetic data generator trained on predominantly
white medical records would not accurately reflect the true disease progression
and outcomes experienced by Black patients.
To address this, representative real-world data must be collected first toensure that AI models do not perpetuate healthcare disparities. Moreover, thesynthetic data landscape in healthcare is fraught with ethical considerations.
While synthetic data offers the potential to accelerate medical research, drug
development, and personalized treatment strategies, it must be used with care to
avoid reinforcing biases and ensuring patient privacy and consent.
The opportunities for bias to enter into the data and machine learning
lifecycle occur at every stage from inception, to System Requirement definition,
data discovery, selecting and developing a model, testing and interpretingoutcomes, and post-deployment/ Impact & Audit.

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https://aiequalitytoolbox.com/library/synthetic-data/15

A multifaceted approach
Addressing gender data gaps in healthcare using AI requires a multifaceted
approach, both in terms of technical solutions and systemic awareness. Here aresome thoughts on a multi-dimensional strategy to fill these gaps, keeping thedata limitations in mind:● Acknowledge the Limitations: First and foremost, any AI solution should
clearly communicate the limitations of the data it's trained on. Users should beaware that predictions or insights may have inherent biases and are not likely
to be as accurate for underrepresented groups, particularly women.
● Collaborate with Experts: Collaborate with gender researchers, sociologists,and clinicians who have expertise in women's health. Their insights can guidedata collection, feature engineering, and model evaluation.
● Crowdsource & Citizen Science: Engage the public, especially women, incollecting and contributing health data, including with wearables. Initiatives
like Apple's Research Kit have shown how valuable citizen-contributed data
can be for medical research.
● Synthetic

Data

Augmentation:

Using

synthetic

data

augmentation

techniques to artificially increase the size of underrepresented datasets. As
noted above, this doesn't replace real data, but it can help improve model
performance by generating synthetic data based on existing patterns. With
important caveats as ‘existing patterns’ can translate into perpetuating
healthcare inequalities.
● Transfer Learning: Use models pre-trained on related tasks or datasets tobenefit from their learned features. This can be particularly helpful if there are
related areas of medicine where more diverse data is available.
● Meta-analysis and Data Synthesis: Conduct a meta-analysis of existingstudies to derive broader insights. Even if individual studies are male-centric,
combining results could help highlight patterns or trends that are relevant towomen.

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● Inclusive Model Development: Design models that explicitly account forgender and other demographic differences. For example, use multi-task
learning where one task could be predicting a medical outcome, and anothertask could be predicting gender, making the model aware of genderdifferences.
● Regular Model Evaluation and Audit: Continuously evaluate and update themodels as new data becomes available. This iterative approach ensures that
AI solutions improve over time and remain relevant. If models ‘go rogue’ inthe wild, retire them.
● Ethical Oversight: Establish an ethics committee or review board focused on
AI in healthcare. This board can assess AI solutions for potential biases,
ensuring that they meet ethical and inclusivity standards.
● Feedback Mechanisms: Implement feedback loops where clinicians andpatients can provide input on AI predictions or insights. Over time, this canhelp in refining the model and making it more attuned to real-world nuances.
● Education and Training: Educate healthcare professionals on the limitationsof AI tools, especially when they're based on historically biased data. This
education ensures that they can make informed decisions and remain criticalof AI outputs.
● Push for Policy Change: Advocate for policies that ensure more inclusive anddiverse data collection in future clinical trials and studies. Over time, this will
help in reducing the data gaps.
By embracing these strategies, AI can be a powerful tool to fill the data gaps inwomen's healthcare, while also ensuring that the solutions remain transparent,
ethical, and continuously improve.
“If used carefully, this technology could improve performance in healthcare and potentially reduce inequities,” says MIT Assistant Professor Marzyeh
Ghassemi. “But if we’re not actually careful, technology could worsen care.”26 27
26
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The downside of machine learning in health care | MIT News | Massachusetts Institute of Technology
In medicine, how do we machine learn anything real?: Patterns

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