South Asians are missing from global health databases: why this matters and what needs to change
South Asian populations are significantly underrepresented in global genomic and health databases, which rely heavily on European ancestry data. This lack of diversity limits the effectiveness of AI-driven medical tools and personalized treatments for South Asian patients.
Why it matters
Data bias in medical research exacerbates health inequities, preventing millions from receiving accurate disease risk assessments and tailored healthcare interventions.
Globally, more than one in 10 adults now live with diabetes. If you have South Asian roots, that risk is even higher, and it hits earlier than it does in many other populations. In India alone, the number of people with diabetes is projected to reach 125 million by 2045 .
Yet, when scientists try to understand why diseases such as diabetes and cardiovascular disease affect South Asians differently, they often have to rely on genetic data drawn from European populations.
Advances in artificial intelligence and machine learning are allowing scientists to mine vast amounts of genomic and health data to detect disease earlier, predict risk, monitor patients and tailor treatments to individuals. But at the heart of this changing landscape lies an old, constant problem – the data used to build these tools lack diversity.
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