The African Microbiome Gap: A Missing Foundation for Reliable Clinical Artificial Intelligence

Clinical Artificial Intelligence (AI) increasingly treats the gut microbiome as a predictive variable for treatment efficacy, toxicity, and disease progression, yet the cohorts on which these models are trained come almost entirely from North America, Europe, and East Asia. We survey the public archive and show that Africa’s absence takes two forms. Coverage: Africa contributes 3.5% of the global human gut metagenome record, with no data from 26 of 54 countries, limiting the pretraining corpus that large genomic foundation models can draw on. Supervision: a six-gate funnel reduces 125 archive-linked African studies to 1 with treatment, outcome, pre- and post-exposure sampling, and participant metadata. To evaluate this gap, we train a Human Immunodeficiency Virus (HIV) serostatus classifier on three separate United States cohorts and test its performance in an African setting. Across four model families, it reaches Area Under the Receiver Operating Characteristic (AUROC) 0.68–0.73 under leave-one-site-out evaluation within the US, and collapses to 0.20–0.25 and 0.55–0.57 in Uganda and Botswana, respectively. Finally, we propose four resource-efficient strategies to close the African microbiome gap: embedding sampling in cohorts that already fund recruitment and follow-up; anchoring design to a defined clinical decision; tiering sequencing depth to the question asked; and shared metadata and collection protocols. Africa lacks both broad sequence coverage and clinically linked cohorts; expanding the pretraining corpus cannot substitute for target-population data needed to develop and validate clinical models.