AI in Healthcare & Science · AI Health Risks and Limitations

Can AI Health Tools Have Biases That Affect Certain Groups Unfairly?

Yes — AI health tools can develop biases when the data used to train them underrepresents certain populations or reflects existing healthcare disparities, potentially causing the tool to perform less accurately, or make less appropriate recommendations, for groups not well represented in that training data.

Medical disclaimer

This page is for general educational purposes only and is not medical advice. It does not replace a consultation with a licensed physician, pharmacist, or other qualified health provider. Always talk to your own care team before starting, stopping, or changing any medication or supplement.

Key takeaways

AI models learn patterns from the data they're trained on, and if that data underrepresents certain populations, the resulting tool can perform less accurately for those groups.
Historical healthcare data can itself reflect existing disparities in access and treatment, which an AI model can inadvertently learn and reproduce if not carefully addressed.
Bias can show up in various forms, including differences in diagnostic accuracy, risk prediction, or treatment recommendations across different demographic groups.
Researchers, regulators, and healthcare organizations have increasingly emphasized the importance of testing AI health tools across diverse populations before and after deployment.
Addressing bias in AI health tools is an active and ongoing area of research, regulatory attention, and industry practice rather than a fully solved problem.

Bias Is a Real and Documented Risk

AI health tools are only as representative as the data used to train them, and this creates a genuine and well-documented risk of bias. If a tool is trained primarily on data from certain populations — say, skewed by age, sex, race, ethnicity, or geographic region — its performance may not generalize as well to patients from groups that were underrepresented in that training data. This isn’t a hypothetical concern: researchers studying medical AI tools have published findings documenting cases where a tool’s accuracy or reliability varied meaningfully across different demographic groups, often traceable to gaps or imbalances in the underlying training data.

This matters significantly in healthcare specifically, where an inaccurate or less reliable tool could translate into real differences in the quality of care or health outcomes experienced by different groups of patients.

How Historical Healthcare Disparities Can Get Baked In

A particularly important and sometimes less obvious source of bias comes from the fact that historical healthcare data itself can reflect pre-existing disparities in how different populations have been diagnosed, treated, or had their symptoms interpreted and taken seriously. If an AI model is trained on this kind of historical data without careful attention to these embedded patterns, it can inadvertently learn and reproduce the same disparities, potentially perpetuating rather than correcting for unequal treatment across different groups. This is a subtler and, in some ways, more concerning mechanism of bias than simple underrepresentation, since it means even a technically well-performing model on average could still encode and replicate unfair patterns present in the historical data it learned from.

An Active Area of Attention, Not a Solved Problem

Awareness of these bias risks has grown considerably within the medical AI research community, regulatory bodies, and healthcare organizations developing and deploying these tools. This has translated into growing emphasis on practices like testing AI health tools across genuinely diverse patient populations before they’re deployed clinically, working to build more representative and diverse training datasets, and conducting ongoing performance monitoring after deployment specifically to catch any signs that a tool is performing unevenly across different demographic groups. That said, addressing bias in AI systems remains a genuinely difficult technical and data challenge, and it would be inaccurate to characterize this as a fully solved problem — it continues to be an active area of research, regulatory attention, and industry practice.

Bottom Line

AI health tools can indeed develop biases that affect certain groups unfairly, typically stemming from training data that underrepresents certain populations or reflects historical healthcare disparities, which is why testing across diverse populations and ongoing performance monitoring have become important, though still evolving, safeguards in responsible AI health tool development.

Go deeper

Important caveats

Not every AI health tool exhibits significant bias, and the degree of bias varies by tool, training data, and how carefully a tool was developed and validated.
Identifying and correcting bias in AI systems is a technically challenging and ongoing process rather than something achieved through a single fix.

Frequently asked questions

How does bias get into an AI health tool in the first place?

Bias typically originates from the training data used to build the model. If certain populations, such as specific racial, ethnic, gender, or age groups, are underrepresented in that data, or if the data reflects historical disparities in how different groups have been diagnosed or treated, the resulting model can learn and reproduce those same patterns, sometimes leading to less accurate or less appropriate outputs for underrepresented groups.

Are there documented examples of bias affecting AI health tools?

Researchers have published studies documenting cases where AI health tools performed less accurately for certain populations compared to others, often tracing this back to how the training data was composed. This has become a well-recognized area of concern and ongoing research within the medical AI field.

What is being done to address bias in AI health tools?

Researchers, regulators, and healthcare organizations have increasingly emphasized practices such as testing AI tools across diverse patient populations before deployment, working to build more representative training datasets, and conducting ongoing monitoring of a tool's real-world performance across different demographic groups after it's put into use.

Sources

[1]U.S. Food and Drug Administration — U.S. Food and Drug Administration
[2]National Institutes of Health — National Institutes of Health

Written by Editorial Team

Last updated July 25, 2026

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