Women’s Bodies Were Never the Standard. And Healthcare Is Still Paying the Price.

When we talk about the female body in medicine, we need to start with an uncomfortable truth.

Women were not the standard.

For decades, clinical trials, medical research, and pharmaceutical development were built around the male body. Male biology was treated as the default. Female biology was treated as a variation. Something secondary. Something complicated. Something inconvenient.

As a result, women have spent generations using medications and undergoing treatments that were never designed for their bodies in the first place.

This is not a historical footnote. It is a systemic failure that still shapes healthcare outcomes today.

When One Body Becomes the Blueprint

Most medications currently prescribed to women were tested primarily on men. Dosages, side effects, efficacy, and risk profiles were optimized around male hormonal stability, male metabolism, and male physiological responses.

Women were excluded because their cycles were considered too “complex”. Pregnancy was seen as a liability. Hormonal fluctuations were treated as noise instead of data.

The outcome is predictable.

Women experience different side effects. Treatments work inconsistently. Symptoms are dismissed as psychosomatic. And when results do not align with expectations, the blame quietly shifts to the patient.

This is where the disparity begins.

Healthcare outcomes are only as good as the data they are built on. And for women, that data has been incomplete by design.

The Cost of Missing Data

When women are excluded from trials, the system loses the ability to understand how female bodies actually respond to medication, stress, lifestyle, and hormonal change.

This leads to misdiagnosis. Overtreatment. Undertreatment. And in many cases, years of medical uncertainty.

I know this personally.

I was misdiagnosed and mistreated for more than a decade. Not because my symptoms were unclear, but because the data frameworks used to evaluate them were not built for women like me. There was no continuous data. No contextual understanding. No longitudinal insight into how my body changed across cycles, stress levels, and life phases.

Instead, there were assumptions. Assumptions are dangerous in healthcare.

There Is No One Size Fits All Female Body

Women do not live static lives.

We have different lifestyles, different stressors, different hormonal rhythms, different cycles, and different biological responses. A woman’s body today is not the same body she had three months ago, or three years ago.

Yet healthcare systems continue to rely on snapshot-based models. Single appointments. Isolated lab results. Short consultations disconnected from daily reality.

This approach fails women precisely because it ignores how female biology actually works.

At YON E, we are building from a different premise.There is no one-size-fits-all female body. And there never was.

Our work is focused on developing approaches that will identify individual patterns. Lifestyle context. Menstrual cycles. Early signals that appear long before symptoms escalate into something that forces medical intervention.

By enabling daily measurement and contextual interpretation of data, we aim to make it possible to detect issues earlier, personalize insights, and reduce the reliance on trial-and-error medicine.

This is not about replacing doctors. It is about creating systems that will give healthcare providers access to more relevant, longitudinal information than has previously been available.

Inclusive Data Is Not Optional. It Is Essential.

When we talk about inclusive data, we are not talking about optics.

We are talking about accuracy.

Women of different ethnicities experience different health risks, hormonal patterns, and treatment responses. Yet many of these women are still excluded from research, underrepresented in trials, or dismissed in clinical settings.

Too often, women feel unheard. Misunderstood. Or simply invisible.

The absence of inclusive data reinforces inequality. It creates blind spots that disproportionately affect women who are already underserved by healthcare systems.

If women are not included in research, healthcare cannot improve for women. It is that simple.

Why Measurement Changes Everything

When it becomes possible to measure what is happening inside the body in real time, the conversation can change.

Instead of vague symptoms, there are patterns. Instead of assumptions, there is evidence. Instead of generalized treatment plans, there is personalization.

With inclusive, consent-based data, healthcare providers can understand what women are actually experiencing across their cycles. They can identify early warning signs. They can prescribe treatments that are relevant to the situation, not based on outdated averages.

This reduces unnecessary medication. It improves outcomes. And it restores trust.

Because when women see that their experiences are reflected in the data, they stop questioning their own bodies.

The Opportunity We Have Right Now

For the first time, we have the technology to change this system.

We can move away from male-centered defaults. We can build datasets that reflect real female biology. We can design healthcare around women’s lived realities instead of forcing women to adapt to systems that were never designed for them.

But this will only happen if inclusion becomes non-negotiable.

Not as a trend. Not as a talking point. As a foundation.

Changing Healthcare Means Changing Who It Is Built For

The disparity in women’s healthcare is not caused by a lack of innovation. It is caused by who innovation has historically been built for.

If we want better outcomes, we need better data. If we want better data, we need women fully included. And if we want women to participate, we need systems they can trust.

Women deserve healthcare that understands their bodies, respects their complexity, and responds to their individuality.

That starts by acknowledging the past.

And it continues by refusing to repeat it.