Hear from our Senior AI Scientist Vaughn Spurrier on the most common reason your medical AI will be rejected by clinicians and how to overcome it. As he writes, data scientists often develop medical machine learning models and neural networks using datasets that contain very different distributions of medical conditions than those seen in real world settings. This discrepancy leads models that perform well on a balanced, curated dataset to become virtually useless in the clinic.
To solve this discrepancy, researchers should validate models for medical applications using two datasets with different class distributions: one that is enriched with rare conditions, useful for generating statistical power; and a second dataset with a condition distribution equal to the distribution the model will face in the clinic.
