Imagine two people walk into a hospital. Same symptoms. Same diagnosis. One receives a treatment that works like magic. The other? Nothing.

Why? Because what’s written in our DNA is as unique as our fingerprint. And the hard truth is: the science that informs healthcare—its research, data, and trials—has been built on just a handful of fingerprints, while billions of others remain invisible.

As genomics rapidly becomes the foundation of medicine—from diagnosing diseases to developing life-saving drugs—we must confront an urgent question: who is being left out?

When I first analyzed open-access genetic datasets back in 2017, I stumbled upon a troubling fact: 95% of those datasets came from individuals of European ancestry. That was a wake-up call.

It wasn’t just a statistical imbalance—it was a systemic flaw. Populations across Africa, Latin America, Asia, and Oceania are missing from the very data that’s driving our genomic breakthroughs. That absence leads to real-world consequences: treatments that don’t work, adverse drug reactions, and diagnostic tools that fail.

One example: warfarin, the world’s most prescribed anticoagulant. In people of African ancestry, one in four experience adverse drug reactions. Why? Because dosing algorithms are trained predominantly on European data. That’s not just a data problem. It’s a healthcare injustice.

My journey began at the Sanger Institute in Cambridge, where I worked on rare developmental diseases. Back then, we lacked the maps and tools to diagnose many of these conditions. But sequencing the human genome changed that. Suddenly, we could read the blueprint of life.

That experience convinced me of genomics’ potential—not just for the few, but for everyone. Every human carries mutations. Every genome holds clues about disease susceptibility, drug response, and more. Genomics should be a universal right. But that can only happen if our science includes everyone.

We talk a lot about “precision medicine.” But how precise can it really be if entire populations are absent from the data? Precision for some is not precision at all.

The lack of diversity isn’t always intentional. It’s often the result of where research happens: in the Global North, among predominantly white populations, with infrastructures that don’t reach remote or under-resourced regions. But the outcome is the same—a system skewed toward a narrow slice of humanity.

One of the most humbling parts of my work has been engaging directly with indigenous communities in Latin America. These encounters changed me. They reminded me that science isn’t just about data—it’s about people.

It’s one thing to run algorithms. It’s another to sit in a village deep in the Amazon and hear a mother say, “Please help us. We want access to medicine. We want our children to be healthy.”

Science must begin with listening. If we want to build trust, we have to show up—not as experts, but as humans. Ethical genomic research must be culturally sensitive, community-driven, and grounded in genuine relationships.

We also need to address how ancestry and sex biases creep into research. Women are underrepresented in trials. Pregnant women are often excluded altogether. Africa, the most genetically diverse continent, is still treated monolithically in many studies.

Even our definitions of “diversity” vary wildly across datasets—from FDA classifications to pharmacogenomics to clinical genomics. There’s no consensus on what counts as representative, and that makes equity nearly impossible to measure.

We need standards. We need metrics. And we need leadership willing to prioritize inclusion—not just as a checkbox, but as a scientific imperative.

I often say that genomics is waiting for its “ChatGPT moment.” That moment when a powerful, complex technology becomes accessible and impactful for everyone—not just specialists.

We’re not there yet. Biology is complex. The genome is only one layer. Add the epigenome, transcriptome, proteome, and microbiome, and the picture becomes exponentially more intricate. But I believe AI can help us untangle that complexity. If—and only if—we include the full diversity of human biology in the training data.

If we do nothing, the future of medicine will be one of stark inequality. Those with wealth and access will benefit from personalized treatments and longer lives. Those without will be left behind.

But that’s not inevitable. There is hope.

The UK, for example, through initiatives like Genomics England and Our Future Health, has made real strides in democratizing access to genomic data—even if its datasets still reflect a predominantly white population. The openness to global researchers is a step forward.

More than technology, what gives me hope is the spirit of collaboration, integrity, and shared purpose I see in communities around the world. The real future of healthcare will be shaped not by algorithms alone, but by people—scientists, clinicians, advocates, and listeners—committed to equity.

To every young researcher, policymaker, or citizen reading this: don’t chase prestige. Chase truth. Chase justice. Your work matters, not just for citations or promotions, but for the lives it can improve.

Science that leaves people behind isn’t science at all. It’s time to build something better.

Because the future of healthcare belongs to all of us. And it starts with listening.