Table 1.

Research questions.

Research questions	Operational definitions
What is the representation of vulnerable individuals in the intended target population for any study on artificial intelligence within primary care?	Vulnerable populations are defined as those with known disparities as described by the following categories: Place of residence (eg, rural) Race, ethnicity (eg, Black) Occupation (eg, coal miners) Gender, sex (eg, transgender) Religion (eg, Amish) Education (eg, high-school only) Socioeconomic status (eg, low income) Social capital (eg, isolation)
How well do current studies on artificial intelligence in primary care report different types of bias that may be perpetuated as health disparities by their systems?	Data extraction elements (Table 2)
What interventions do current studies on artificial intelligence in primary care use to address harmful effects of pre-existing biases in their systems?	Example interventions are listed below: Preprocessing Modified data sources Preprocessing data for fairness Model development Demographic parity Equalized odds/opportunity Disparity regularization Counterfactual fairness Postprocessing Subgroup analysis Meta-regression Quality assurance