AI and Healthcare: Learn from the Intellectual History of Technological Transformation

Artificial intelligence (AI) is reshaping the landscape of research and innovation across the globe. The economic framework—Acemoglu & Restrepo’s task-based model—distinguishes displacement from reinstatement and treats AI as a binary force. AI technologies is dynamicly changing, and changing fast. How societies have responded to general-purpose technologies—a tradition spanning from Tocqueville’s analysis of democratic institutions and social reform [Drolet 2003] through the Saint-Simonian industrial programme and Chevalier’s conscious industrialism to Leroux’s circular economics, encompassing the interface between technology and the natural environment?

We propose three models, as shown in Figure 1. Each maps to a recognisable type of frontier healthcare institution and has distinct implications for the physical, mental, and social dimensions of health.

(1) The Extractive Model (liberal political economy): The superhospital concentration model. Knowledge flows one way—from workers and communities into AI systems. AI capability is hoarded at elite centres while the referring workforce is depleted and health inequity widened. This model is value-destroying—physically (reduced access at the periphery), mentally (deskilling, burnout, loss of professional agency), and socially (care communities fragmented, knowledge networks broken). Where extractive AI automates entry-level roles without creating new pathways, it risks collapsing healthcare career ladders for the next generation. 

(2) The Infrastructure Model (Chevalier’s conscious industrialism): This model is value-creating. Drolet’s analysis of Chevalier’s environmentally conscious political economy shows that sustainable infrastructure requires understanding humanity’s relationship to nature—directly relevant to climate-resilient healthcare AI.

(3) The Circular Model (Leroux’s circulus): This model is value-regenerating: the circulus is not a metaphor—it is a measurable design principle for AI systems that regenerate the workforce’s capability rather than deplete it. The LMIC equity model—where AI must reduce inequality because workforce scarcity leaves no alternative. Leroux’s original vision was that what is taken from people must be returned to nourish complete humanity, not merely material productivity but physical, mental, and social flourishing.

History shows that these three models expose the limits of their predecessors.​