When Benchmarks Rot: Why Static ‘Gold Labels’ Are a Clinical Liability
Opening — Why this matters now Clinical AI has entered an uncomfortable phase of maturity. Models are no longer failing loudly; they are failing quietly. They produce fluent answers, pass public benchmarks, and even outperform physicians on narrowly defined tasks — until you look closely at what those benchmarks are actually measuring. The paper at hand dissects one such case: MedCalc-Bench, the de‑facto evaluation standard for automated medical risk-score computation. The uncomfortable conclusion is simple: when benchmarks are treated as static truth, they slowly drift away from clinical reality — and when those same labels are reused as reinforcement-learning rewards, that drift actively teaches models the wrong thing. ...