From faster diagnosis, tailored treatments, and better management of scarce resources, AI is touted to revolutionise healthcare. However, hurdles like biased datasets that risk discrimination, distrust amongst healthcarers, accountability, and who should own and take responsibility for the technology, threaten that potential.

What if the key to overcoming thechallenges of AI in healthcare lies in the very quality that makes it so intriguing—its ability to find patterns in data that surpasses human capabilities? AI excels at identifying connections between disparate pieces of information, uncovering insights that can transform our understanding of the world. This is what intersectionality has long argued for in healthcare, seeing people’s health and well-being at the nexus of multiple social positions (e.g. gender, class, race, etc).  

For AI to truly improve healthcare, and turbocharge innovation, we need to apply the principles of intersectionality. This means bringing in diverse voices—not just as subjects of the technology but as active participants in its development. By doing so, we create a system that is more aware of the potential harms AI can cause and how to mitigate them, while also co-creating solutions that genuinely improve human health outcomes.