Algorithmic Bias and Health Equity: A Conceptual Model of How Bias Enters, Propagates and Compounds in Medical AI Across the Data-to-Deployment Pipeline
DOI:
https://doi.org/10.68854/7evf1x91Keywords:
algorithmic bias, health equity, medical AI, representation, data pipeline, fairness, dermatology, medical imaging, African populationsAbstract
Bias in medical artificial intelligence (AI) is most often traced to training data, and the usual remedy is to collect more or better data. This theoretical paper argues that bias can enter at every stage of the data-to-deployment pipeline, from the social context of data generation to monitoring after deployment. It also argues that bias accumulates and compounds across stages, so a correction at one stage can leave much of the disparity in place. An integrative review was combined with theory synthesis, and the population of interest was the peer-reviewed literature on algorithmic bias and health equity in medical AI. Seven stage-aligned search strings were run in PubMed/MEDLINE and supplemented by eight OpenAlex queries. Records were sampled purposively, by relevance in PubMed and by citation count in OpenAlex. Forty-nine journal articles published between 2023 and 2026 were included; each was verified against its Crossref publisher record and screened for retractions, corrections and expressions of concern. The evidence covered under-representation in dermatology and medical imaging, emerging work in genomics and clinical language models, evaluation studies and frameworks for fairness and equity. Articles were coded deductively to pipeline stages and bias mechanisms and summarised as frequencies. The evidence was concentrated in the United States (30 of 49 first authors), with one article from Africa. Propagation ( n = 35) and entry ( n = 30) were often addressed, but compounding was explicit in only 10 articles. The resulting Bias Cascade Model has eight stages, three entry routes, four propagation channels and three conditions under which bias compounds. The model is accompanied by an equity check for each stage and six testable propositions about how bias accumulates, compounds and can be interrupted. The African context is central: populations under-represented in training data are those on which deployed systems can be expected to perform worst. The paper closes with implications for health ministries, regulators and research institutions.
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