Why Radiology AI Cannot Afford Poor Data Annotation

Radiology is one of the clearest domains where the consequences of model error are not theoretical. They are clinical, immediate, and profoundly real. When models attempt to detect a brain hemorrhage in a CT scan or identify characteristics of a tumor on an MRI, accuracy is not a luxury. Accuracy is the difference between the right clinical decision and the wrong one. And the only way to engineer accuracy is by starting with high-quality labeled data.

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Yet the traditional data labeling supply chain is fundamentally insufficient for this domain. It was not built for the complexity, nuance, or consequences of medical imaging. Radiology images require deep domain expertise to interpret correctly. Variants, subtypes, ambiguous margins, artifacts, patient history context, and modality-specific nuance all dramatically influence how the same image can be annotated. Even board-certified radiologists do not agree perfectly with each other.

The assumption that any single annotator represents a gold standard in radiology is inaccurate. And it is dangerous.

This becomes even more critical as models shift beyond pure supervised training and into evaluation loops for foundation models and generative medical models. Evaluation cannot be an afterthought. It must be as scientifically rigorous as training itself. If we evaluate LLM-based medical models using noisy or unreliable labels, we cannot trust the benchmarks we are producing. The illusion of model quality is worse than not measuring quality at all.

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Radiology offers repeated examples of this in practice. Distinguishing subtle stroke indicators in CT perfusion studies. Characterizing faint ground-glass opacities on chest CT. Identifying microcalcifications or architectural distortion on mammography. Assessing lesion evolution across serial MRI scans. These are not mechanical tasks. They are interpretation tasks, where disagreement is expected, and nuance determines whether a model makes the correct clinical inference.

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At Centaur, our view has always been that quality must be engineered. It cannot be hoped for. That is why we do not rely on any single annotator. Instead, we harness collective intelligence to outperform individuals. We statistically identify who performs best on radiology data, reward them, rank them, and benchmark them. When labels matter this much, we need proof that a label is strong, not belief or assumption. This is the foundation for building quality that holds up to scientific scrutiny.

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Radiology is where this becomes nonnegotiable. It is the perfect example of a complex domain where reliability in data annotation directly determines performance in model evaluation and training. Lives, diagnosis accuracy, and treatment decisions depend on it. Foundation model evaluation is only trustworthy if the data behind the evaluation is trustworthy. The bar cannot be average. It must be engineered to be consistently higher than any single human.

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This is what "accuracy first" means when it impacts real patients.

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For a demonstration of how Centaur can facilitate your AI model training and evaluation with greater accuracy, scalability, and value, click here: https://centaur.ai/demo

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