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While AI-driven healthcare solutions have proven their impact and reliability, healthcare organizations struggle to achieve the desired value from their investments in AI.
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The most promising use cases are in early diagnosis and risk stratification for chronic disease, but AI also has the potential to revolutionize drug discovery and health system operations.
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AI’s impact on global health depends on three enablers: usable, representative data; trustworthy design; and, scalability.
Before GPS, drivers relied on the humble printed map. GPS changed everything. The technology wasn’t perfect — it was ignorant of new roads, temporary closures and traffic conditions — but we adopted it because it beat the status quo. The occasional glitch wasn’t the worst thing in the world and, while the public might not have fully understood satellite-based radio navigation, they didn’t need to.
Self-driving technology, however, faces an uphill climb given its complexity, life-or-death stakes and the need to work everywhere and in all conditions. AI in healthcare resembles self-driving vehicles in that the data and assumptions used to build algorithms are obscure, the consequences of a clumsy algorithm are dire and, to transform healthcare, solutions must be deployable at scale.