Giving Compass' Take:
- Shelley Wehmeyer discusses how AI can address health equity disparities through inclusive data analysis, equitable treatment strategies, and streamlined administrative processes.
- How can funders ensure that AI is developed and deployed to intentionally reduce health inequity rather than exacerbate it?
- Learn more about key issues in health and how you can help.
- Search our Guide to Good for nonprofits focused on health in your area.
What is Giving Compass?
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Healthcare inequities and disparities in care are pervasive across socioeconomic, racial, and gender divides. As a society, we have a moral, ethical, and economic responsibility to close these gaps and ensure consistent, fair, and affordable access to healthcare for everyone. AI can help mitigate health disparities, but it is also a double-edged sword. Certainly, AI is already helping to streamline care delivery, enable personalized medicine at scale, and support breakthrough discoveries. However, inherent bias in the data, algorithms, and users could worsen the problem if we’re not careful.
That means those of us who develop and deploy AI-driven healthcare solutions must be careful to prevent AI from unintentionally widening existing gaps, and governing bodies and professional associations must play an active role in establishing guardrails to avoid or mitigate bias.
Here is how leveraging AI can bridge inequity gaps instead of widening them.
How AI Can Help Mitigate Health Disparities in Clinical Trials
Many new drug and treatment trials have historically been biased in their design, whether intentional or not. For example, it wasn’t until 1993 that women were required by law to be included in NIH-funded clinical research. More recently, COVID vaccines were never intentionally trialed in pregnant women—it was only because some trial participants were unknowingly pregnant at the time of vaccination that we knew it was safe.
A challenge with research is that we do not know what we do not know. Yet, AI helps uncover biased data sets by analyzing population data and flagging disproportional representation or gaps in demographic coverage. By ensuring diverse representation and training AI models on data that accurately represents targeted populations, AI helps ensure inclusiveness, reduce harm and optimize outcomes.
How AI Can Help Ensure Equitable Treatments
It’s well established that Black expectant mothers who experience pain and complications during childbirth are often ignored, resulting in a maternal mortality rate 3X higher for Black women than non-Hispanic white women regardless of income or education. The problem is largely perpetuated by inherent bias: there’s a pervasive misconception among medical professionals that Black people have a higher pain tolerance than white people.
Read the full article about AI mitigating health disparities by Shelley Wehmeyer at Unite.AI.