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Currently, diagnosing a patient with systemic lupus erythematosus (SLE) is a complex process that compares potential lupus with other conditions. It can be challenging and delayed by a period of time, which increases patient uncertainty, referrals, healthcare utilization, increased flares, and organ dysfunction. In this study, machine learning (ML) via artificial intelligence tools based on patient data was used to develop an algorithm to help with SLE diagnosis.