Can AI bias tackle sex and gender bias in healthcare?

The accuracy of AI-assisted clinical diagnoses is completely reliant on the robustness of the underpinning data sets. Without actively accounting for sex and gender bias in historical data sets, AI may contribute to missed or misdiagnoses.

Fortunately, adjusting for such biases appears to lead to better healthcare outcomes for women.

For example, the traditional risk assessment score for heart attacks, the Global Registry of Acute Coronary Events (GRACE), was updated in 2022 to incorporate AI predictive models that account for sex-specific disease characteristics.

This update has revolutionised the performance of this assessment tool. The success stems from separate analyses of male and female data—which guides more female patients to lifesaving early intervention, helping overcome structural biases in patient management.

Read more: ApacMed CEO Harjit Gill on how AI is revolutionising healthcare

A practical example of an AI model designed to address and reduce gender bias is SMARThealth Pregnancy GPT. This tool, developed by The George Institute for Global Health, aims to improve access to guideline-based pregnancy advice for women living in rural and remote communities in India.

The concept was to develop a large language model chatbot that would be contextually sensitive and clinically accurate—and avoid entrenching harmful stereotypes.

The George Institute team worked closely with community health workers, clinicians and women living in rural communities, to co-create and refine the tool’s algorithm. Clinicians also scored AI-generated answers on accuracy, appropriateness for community health workers, completeness and risk of bias, which helped improve the chatbot’s responses.

The chatbot showcases AI’s potential in building healthcare worker capacity and enhancing health education in resource-limited settings—while avoiding bias and promoting women’s rights.

Read more: Shaping a safer future: How to engage and educate men and boys to end gender-based violence

Gender-sensitive AI development could similarly improve countless other medical technologies that rely on data diversity and integrity for accuracy: for example, tailoring personalised treatments, predicting treatment responses, performing certain robot-assisted surgeries, monitoring patients remotely, virtual healthcare and accelerating drug discovery.

Initiatives to advance improved sex and gender equity in healthcare have begun to emerge in recent years, too. They include the newly launched Australian Centre for Sex and Gender Equity in Health and Medicine and the UK Medical Science Sex and Gender Equity.

These programmes actively advocate for routine consideration of sex and gender from discovery to translational research, including AI applications, to ensure scientific rigour as a robust foundation for advancing health and medical care.

AI is the future of healthcare, and we can’t afford to replicate the past mistakes of health inequities perpetrated by ignoring sex and gender. It is time to program AI to chart our course toward an ethical destiny.

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