Bias doesn't stop with the data set: it's in the deployment

When we talk about bias in healthcare AI, the conversation almost always starts and ends with data. We scrutinize training sets, audit algorithms, and develop fairness metrics. But there’s also a little-known bias: deployment bias. This is equally dangerous.

Even well-trained, carefully calibrated AI models can exacerbate inequality depending on where and how they are deployed. To be clear, by AI I mean systems that analyze clinical data such as patient images, audio recordings, and medical histories, not administrative tools like transcription or scheduling assistants. Too often, advanced tools are first introduced in well-resourced urban health systems—facilities with robust digital infrastructure, adequate staff, and rigorous institutional feedback loops. Meanwhile, rural hospitals, community clinics and safety net providers are waiting. Sometimes it's years.

But there’s a deeper issue here: Deployment bias doesn’t just affect who benefits from AI, it also affects how future AI is trained. If AI tools are rolled out primarily in wealthy urban centers, the data they generate will reflect those populations, workflows, and outcomes. This data is then fed back into next-generation models, creating a feedback loop that further marginalizes underrepresented communities. In other words, where we deploy AI today determines who will be represented in tomorrow’s algorithms.

This is not just a rollout timeline issue. This reflects a deeper gap in how we think about innovation. Those communities that stand to benefit most from clinical decision support, diagnostic enhancement, or remote monitoring tools are the last to receive them. Not because the technology isn’t ready, but because we assume the infrastructure isn’t. This assumption itself is a bias. A June 2025 scoping review of U.S. rural health research found only 26 peer-reviewed studies on AI tools in rural settings, 14 of which focused on predictive models and 12 on infrastructure. None of the studies examined generative AI in real-world rural deployments, with half highlighting insufficient data and analytics capabilities as key barriers to development and validation. A July 2025 article titled “The Growing Divide in AI-Powered Care” noted that AI remains “concentrated in metropolitan academic centers, leaving rural communities behind.” The report notes that rural hospitals face infrastructure constraints and few AI projects have advanced beyond design and practical application in these areas.

Much of my career has been focused on increasing access to care, especially in places where health care is only a few hours away, not just around the block. This work shows me how transformative technology can be, but only if it reaches those who need it most. We cannot claim that AI is democratizing care while limiting its scope to zip codes that already have optimal access.

Deviations in deployment are not malicious. But if we don’t call out this bias and explain it, we risk reinforcing a two-tiered system in which AI delivers better outcomes for some people and does nothing for others.

Equity must be built into deployment strategies from day one, not viewed as a future retrofit. This means prioritizing not only the inclusivity of your data, but also the inclusivity of your delivery, and recognizing that inclusive deployment is the foundation for inclusive data sets. Because ultimately, where we choose to deploy AI sends a message about whose health we value and whose data we deem worthy of learning from. For those of us building the future of healthcare, this choice should never be an afterthought.

Photo: Klaus Vedfelt, Getty Images

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