MediMath Science

Before starting this blog, I should note that I am a retired professor and therefore inevitably carry certain biases, as is often the case for academics with limited direct experience in industry. When people in academia talk about the development of medical AI, the discussion often drifts toward higher resolution, more accurate diagnosis, and fully automatic or end-to-end autonomous models. This tendency is understandable. Academic incentives reward measurable performance improvements, benchmark dominance, and methodological elegance. However, this perspective quietly overlooks the force that ultimately determines whether a technology survives outside the laboratory: economics. Healthcare systems do not evolve in ideal conditions. They evolve under demographic pressure, workforce shortages, rising capital and maintenance costs, and reimbursement systems that lag far behind technological ambition. Aging populations increase demand precisely when the number of available specialists decl...

A common pattern is emerging across multiple institutional sectors, including universities and research institutions. Policymakers and administrators are increasingly debating how to retain the valuable skills of senior talent approaching retirement. In the short term, such protective measures are effective: they enhance stability, preserve accumulated experience, and delay the loss of expertise. Over time, however, less visible costs accumulate. Talent turnover declines, entry pathways for younger scholars narrow, innovation slows, and institutions gradually trade long-term vitality for short-term stability. The current debate surrounding the role of distinguished professors over the age of 65 exemplifies this broader structural problem. It is often framed as an ethical dispute or an issue of age discrimination. In reality, it is neither. At its core, this is a question of system design—how a national research ecosystem balances protection with circulation. One point must be stated cl...