Geography, Not Just Algorithms: Why AI Radiology May Lag on Global Health Equity
A KevinMD commentary argues that AI in radiology could either widen or narrow global health inequities depending on how it is deployed. The article frames access, infrastructure, and local relevance as the real determinants of whether imaging AI helps underserved populations.
The global health equity conversation is a useful corrective to the tendency to treat AI as universally beneficial by default. In radiology, access to scanners, trained staff, internet connectivity, and maintenance support can matter as much as model accuracy.
That means a tool that performs well in a large academic hospital may have far less impact in settings where the limiting factor is not interpretation, but basic imaging access. If AI only improves care where infrastructure is already strong, it risks amplifying existing inequities.
The article’s value is in insisting that deployment context matters. Algorithms trained on one population may underperform in another, and models designed for high-resource settings can be poorly matched to the realities of global health systems.
The broader lesson is that health equity is not an add-on to AI policy; it is a design requirement. If radiology AI is going to matter worldwide, it must be built and evaluated with the constraints of low-resource care in mind from the start.