Source: a16z
The article identifies a critical constraint in AI-assisted medicine: algorithms can ace standardized tests but still fail in clinical practice because the human experts needed to label training data, validate outputs, and catch errors are themselves scarce and expensive. The bottleneck sits in the human judgment layer, where radiologists, pathologists, and specialists must continuously annotate edge cases and real-world variations that no test can fully capture. As healthcare systems deploy AI at scale, they're discovering that the limiting factor isn't model performance but the availability of credible human oracles to ground truth the system and maintain accountability when stakes are clinical outcomes.