Box 1.

Glossary of key terms

Term	Definition
Artificial intelligence (AI)	An umbrella term referring to computational technologies that automate tasks typically performed by humans
Machine learning	A subset of AI that refers to models that can learn from examples without the explicit programming of rules
Healthcare AI	An umbrella term referring to AI for use in the health sector (i.e., disease surveillance, diagnostics and treatment, resource allocation, delivery of health services, workflow, etc.)
Protected group	Groups that face discrimination due to a shared social characteristic that are protected under the federal legal code (i.e., race, gender, age, ability, etc.)
Algorithmic bias	An algorithm’s performance, allocation, or outcome for a protected social group puts them at a (dis-)advantage with respect to the unprotected social group
Health equity	The ability of all patients to attain their full health potential is the same across all groups [36]
Development	Creation of the model: a process that encompasses data pre-processing, model training/validation/testing efforts
Validation (regulatory)	Assessment of model performance prior to its formal implementation
Implementation	Integration of the AI model into the healthcare setting for real-world use
Maintenance	Updates made to the AI model after it is in real-world use to assure a continued high-quality performance
Training	A process where the model learns trends or categories from data
Validation (model)	A process that confirms the generality of the trained model and explores different hyperparameter choices
Testing	A process that evaluates model performance on an unseen dataset
Pre-training	A process that trains a model on a large, non-specific dataset prior to subsequent fine-tuning  on the actual dataset to improve overall performance
Federated learning	Each institution trains a model using their home data and the model weights are communicated to a centralized server to develop an aggregate model; there is no sharing of protected health information
Cyclic weight transfer	An institution trains a model using their home data and passes the updated model weights to the next institution, the process repeats until all institutions have participated; there is no sharing of protected health information
Bias accounting	The process of measuring bias, when applicable to the algorithm’s intended use case
Bias mitigation	The process of correcting for bias, when applicable to the algorithm’s intended use case
Positive predictive value	The likelihood that if you screen positive that you actually have the disease
Negative predictive value	The likelihood that if you screen negative that you actually do not have the disease
Equalized odds	No difference in sensitivity and specificity across all groups
Predictive parity	No difference in positive predictive value rates across all groups
Demographic parity	No difference in positive outcome rates across all groups
Validation (AI lifecycle)	Evaluation of model performance prior to formal implementation
Interpretability	The degree to which the decision process of AI is understandable to humans
Continuously learning AI	AI that can update in real-time to learn from incoming data