AI mirrors human cognitive biases, not just factual knowledge.

AI in healthcare amplifies existing gender and cultural stereotypes, worsening inequality.

Gender-coded AI designs reinforce societal stereotypes, impacting trust and care experiences.

Many governments suggest that artificial intelligence (AI) will transform healthcare by improving diagnosis, treatment planning, and administrative efficiency1. However, these claims far outstrip the reality, and not just because of the well-rehearsed practical and ethical problems involved2. Just as important to consider as these practical/ethical worries, not to mention the dearth of good data to back up the somewhat grandiose claims, is that integration of AI into healthcare may reinforce inequalities in healthcare for minoritised groups—especially women. Understanding this problem is essential for ensuring effective use of AI—if, indeed, we must go down that route.

AI systems draw on the large datasets available to them—this is what they "learn" from. Straight away we can see a problem—women’s health issues are under-researched and under-represented in published literature, and women from marginalised groups are especially under-represented in these datasets3. This can lead to gaps in the accuracy and reliability of the datasets from which AI outputs are generated. In turn, this can affect clinical decision-making through biased AI outputs influencing diagnoses and treatment recommendations. The result of this under-representation is inequitable performance—an AI-driven healthcare system that works better for some groups than others3.

The Myth of AI Neutrality

Beyond this performance problem, AI is often thought of as a "neutral" tool, impartially categorising and summarising data, offering faster diagnoses, better predictions of outcomes, and more efficient services. If it does so unequally, it is assumed that this is due to problems with the data input, and not the system. However, research increasingly suggests that AI is far from neutral, but reflects the same psychological biases, stereotypes, and stigmas that shaped the humans who underlie the tool3-5. Remember, there is nothing truly creative about AI—it does what it is told, as interpreted through the programmer’s lens. When used in healthcare, existing biases impact what AI does, and have important consequences, particularly for........

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