Globally, health systems are underperforming across four dimensions: effectiveness, efficiency, equity, and responsiveness of the outputs produced in terms of public health and individual medical services. Despite rising health expenditures and continued scientific innovation, analysis of care cascades for hypertension and diabetes show that only a minority of affected individuals achieve clinical control across all country income groups. This underperformance stems from two structural challenges: innovation misalignment, in which delivery models have not evolved to match scientific advances and innovation, and institutional logic (i.e., the inherited structures and incentives of health systems), in which systems designed for managing acute infectious diseases, and maternal and child health struggle to manage chronic illness. Artificial intelligence (AI) provides an opportunity to address these shortcomings. Evidence from randomized controlled trials demonstrates that AI can improve cancer detection to improve effectiveness, reduce documentation burden to raise efficiency, close screening gaps in underserved populations to enhance equity, and optimize emergency department throughput to increase responsiveness. However, algorithmic bias threatens to widen health disparities if left unaddressed. This seminar presents a framework for AI’s role in health system transformation, reviews current evidence, and proposes a 6A framework in relation to critical considerations (Affordability, Accessibility, Applicability, Algorithmic bias, Agility, Accountability) for scaling AI equitably. The transformational potential of AI will be realized only if regulation keeps pace with innovation and deliberate efforts are made to ensure that all populations benefit.
Atun et al. (Thu,) studied this question.