This literature review dives into racial and ethnic disparities in predictive accuracy of machine learning (ML) algorithms which were designed to forecast 30-day complications following total joint arthroplasty (TJA). Researchers utilized a national database comprising 267,194 TJA patients from 2013 to 2020, organized by race and Hispanic or non-Hispanic ethnicity. The analysis involved histogram-based gradient boosting (HGB) and random forest (RF) models, which showed high accuracy in the non-Hispanic White group (AUC=0.86). However, predictive accuracy declined in minority groups, with AUC values ranging from 0.67 in the American-Indian population to 0.79 in Asian non-Hispanics. The research highlighted significant calibration and accuracy issues, especially within smaller-sized minority cohorts.