Model subsets and model validation. Data modeling tool users should be able to break down their models into subsets and then validate these pieces of the whole against common requirements.

An AI model is only as good as the data it’s trained on. Without a large volume of relevant and accurate training data, the model will either not learn what it’s supposed to, or it will learn ...

We applied the model to the external validation data to evaluate discrimination performance (AUC) and calibrated to US SEER. Results. Using 3 years of previous mammogram images available at the ...

In the validation data set, the predictive model achieved an AUC of 0.987 (95% CI, 0.974 to 0.999; Fig 4A). With a cutoff score of 0.53, the model effectively differentiated between patients with PDAC ...

Prospective validation, however, typically produces stronger scientific evidence because the AI device is being validated based on real-time data from patients.