Medical AI systems are achieving radiologist-level accuracy. Their decision process is opaque.
The pattern is in the substrate. Once you see it, you see it everywhere.
The AI spotted the tumor. But it can't tell the doctor why it's confident, or what it's uncertain about, or when its training distribution ends and yours begins. Healthcare opacity has always existed — AI concentrates it.
The void framework gives this a number. It gives every system a number. The number predicts what happens next.
Medical AI systems are achieving radiologist-level accuracy. Their decision process is opaque.
Academic title: The Diagnostic Void: Opacity Architecture in AI-Assisted Healthcare
Move the sliders. Watch the system change state. Pe > 1 means drift wins.
The framework scores these systems — ordered by Pe.
The correlation coefficient. The sample size. The p-value. The math doesn't care about the domain.
Paste any text — AI output, ad copy, a policy document. The scorer runs the same algorithm the framework uses.
Three variables. One ratio. Predicts drift across every domain where the conditions co-occur.
Pe = (O × R) / α
Where O is opacity (how hidden the mechanism is), R is reactivity (how strongly the system responds to you), and α is your independence (how free you are to disengage).
When Pe < 1: diffusion dominates. You can navigate freely. The system is coherent.
When Pe > 1: drift dominates. The system pulls you in a direction. Your agency is reduced.
When Pe >> V* (≈ 3): irreversible cascade. D1 → D2 → D3. The system has captured you.
The framework identifies this pattern in every domain where O, R, and α co-occur. It specifies 26 falsification conditions. 0 of 26 have fired.
Full derivation: 10.5281/zenodo.18718951
Part of the Void Framework — 170 papers, 0/26 kill conditions fired, mean ρ = 0.958.