Artificial intelligence (AI) is often hailed as the next frontier in healthcare, promising to revolutionize how we diagnose, treat, and manage diseases. But as with any powerful tool, its impact depends on how—and for whom—it is used. Recent discussions among global health experts have brought a crucial message to the forefront: AI can bridge global health gaps, but only if equity is at the heart of its development and deployment.
Imagine a doctor working in a remote village, far from the nearest specialist. With AI-powered mobile apps, that doctor could have the expertise of the world’s best cardiologists and pulmonologists at their fingertips, ready to assist with complex cases. This isn’t science fiction—it’s the vision of Alexandre Chiavegatto Filho, a professor of machine learning in health at the University of São Paulo. His team is already developing tools that allow frontline doctors to access AI support through smartphones, even in places without electronic medical records.
Jiho Cha, a South Korean parliamentarian and physician, shares a similar hope. He sees AI as a way to scale up health services in fragile settings, where doctors are scarce and health systems are stretched thin. By combining AI with fintech and blockchain technologies, Cha believes we can improve health financing and delivery, making healthcare more efficient and transparent.
But there’s a catch. Both experts warn that if AI is left unchecked, it could actually widen the very gaps it aims to close. Algorithms trained on data from wealthier populations often perform poorly for low-income groups, potentially leaving the most vulnerable behind. As Filho cautions, “If you leave AI by itself, it’s probably going to increase inequality.”
So, how do we ensure AI works where it’s needed most? The answer lies in diversity and accessibility. AI systems must be trained on locally relevant data, reflecting the realities of different communities. They must also be designed to function in low-resource settings, where digital infrastructure may be limited.
Actionable Takeaways:
- Invest in local data collection: Encourage the development of datasets that represent diverse populations, especially those in underserved regions.
- Support digital infrastructure: Ensure that healthcare facilities in low-resource areas have the tools and connectivity needed to use AI solutions.
- Promote inclusive policy: Policymakers should prioritize equity in AI regulations, ensuring that new technologies are accessible and beneficial to all.
- Foster cross-sector collaboration: Combining AI with fintech and blockchain can unlock new ways to finance and deliver healthcare, especially in fragile settings.
The opportunity is enormous, but so is the responsibility. By putting equity at the center of AI innovation, we can harness technology to close health gaps—rather than widen them.
Summary of Key Points:
- AI can make expert healthcare accessible in remote and underserved areas.
- Without careful oversight, AI risks increasing health inequalities.
- Training AI on diverse, local data is essential for equitable outcomes.
- Combining AI with fintech and blockchain can improve health delivery and financing.
- Policymakers and innovators must prioritize equity to ensure AI benefits everyone.