The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic research to applications that are deployable in clinical settings requires the ability to generalize and high reproducibility, which are contingent on a rigorous and sound methodology for the development and evaluation of ML models. This article describes the fundamental concepts and processes for ML model evaluation and highlights common workflows. It concludes with a discussion of the requirements for the deployment of ML models in clinical settings.
Keywords: Ability to generalize; Artificial intelligence; Cross-validation; Deep learning; Evaluation; Machine learning; Reproducibility; Validation.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.