Participant Flow Diagrams for Health Equity in AI Research

Abstract

Abstract Biases in sample creation can arise at any study phase, including initial patient recruitment, exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflect the underrepresentation or exclusion of demographic groups historically disadvantaged in medical research. The use of non-representative samples to construct clinical algorithms in artificial intelligence (AI) and machine learning (ML) applications may further amplify this selection bias. Building on the “Data Cards” initiative for transparency in AI research, we advocate for the addition of a detailed participant flow diagram for AI studies, emphasizing the need to detail excluded participant demographic characteristics at every study phase. This tracking of excluded participants enhances understanding of potential algorithmic biases before their clinical implementation, and thus deserves to be detailed in any medical AI study. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we can better gauge the potential inequity embedded in AI applications, facilitating more reliable and equitable clinical algorithms.

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