Confusion in the Matrix: Going Beyond the Roc Curve

Abstract

Artificial intelligence algorithms are being created both investigationally and commercially. Evaluation of their performance is important for developers, investigators, clinical physicians, and regulatory agencies. No clear consensus exists on what metrics are best for algorithmic evaluation for AI and ML applications in radiology. We review the basics of the confusion matrix, continue to single number summary values such as accuracy, F1 score, and ɸ coefficient, and then discuss Receiver Operator Curves and their derivatives, Precision Recall Curves, and Cost Curves. Recommendations are made for potential future directions and what currently may be best practices in algorithmic evaluation metrics.

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