ABSTRACT
Standardized and thorough model reporting is an integral component in the development and deployment of machine learning models in health care. Model reporting includes sharing multiple model performance metrics and incorporating metadata to provide the necessary context for model evaluation. Thorough model reporting addresses common concerns about artificial intelligence in health care including model explainability, transparency, fairness, and generalizability. All stages in the model development lifecycle, from initial design to data capture to model deployment, can be communicated openly to stakeholders with responsible model reporting. Physician involvement throughout these processes can ensure clinical concerns and potential consequences are considered.