WHY FOUNDATION MODELS IN PATHOLOGY ARE FAILING (AND WHAT COMES NEXT)

Summary from the TLDR AI newsletter

Optimization for the wrong target produces excellent results at that target while failing at the actual problem. Pathology needs optimization for clinical utility, generalization across institutions, and robustness to real-world conditions. The next generation of pathology AI will be smarter about what size and architectural approach actually fit the problem. It will be smaller, more interpretable, more rigorously validated, and more aware of its own limitations.