The use of artificial intelligence in advancing molecular biology in Africa: a narrative review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable — no pooled quantitative effect sizes reported; findings are descriptive and qualitative

• Primary Outcome: Narrative synthesis of AI applications across six molecular biology domains in African research contexts, with identification of enabling initiatives and persistent structural barriers to equitable AI-driven discovery

• Nnt Or Sensitivity: Not applicable for this review type; the review references primary studies (e.g., AlphaFold2 protein structure prediction, H3Africa GWAS outputs) that contain quantitative performance metrics, but these are not synthesised or pooled within the review itself

• Confidence Interval: Not reported — narrative review design precludes confidence interval estimation

Clinical Application

The review's recommendations — including population-representative pan-genome references, sustained computational infrastructure investment, interdisciplinary training programmes, and African-led governance frameworks — are feasible in principle but require substantial long-term funding commitments and international partnership. Near-term clinical translation of AI-driven molecular discoveries into African healthcare systems faces significant implementation barriers including laboratory infrastructure, regulatory capacity, and health system integration. Australia has direct relevance to this review through several pathways. First, Australian genomic research institutions (including AGHA, WEHI, and the Garvan Institute) are active in global genomics consortia and should consider African genomic diversity in training datasets for AI tools used in clinical genomics. Second, the TGA's emerging framework for AI-based medical devices and Software as a Medical Device (SaMD) should be informed by equity considerations analogous to those raised in this review — specifically, whether AI diagnostic or pharmacogenomic tools validated on predominantly European datasets perform equitably in Australia's Aboriginal and Torres Strait Islander populations, who face analogous underrepresentation in global genomic reference datasets. Third, RACGP guidelines on genomic medicine and the MBS genomic testing items should be reviewed in light of evidence that AI variant interpretation tools may perform differentially across ancestral populations. The PBS does not currently list AI-specific genomic tools, but pharmacogenomic testing relevant to PBS-listed medicines (e.g., thiopurines, clopidogrel, codeine) is increasingly AI-assisted and subject to the same equity concerns raised in this review. Researchers, clinicians, and policymakers engaged in genomic medicine, infectious disease genomics, pharmacogenomics, and precision medicine in African healthcare and research settings; also relevant to global genomics researchers working with or on African populations

Abstract

Artificial intelligence (AI) is rapidly becoming a core methodological pillar of molecular biology and precision medicine, and Africa is a uniquely consequential setting for this transition because the continent combines the world's greatest human genomic diversity with the most severe underrepresentation of that diversity in the datasets and reference resources on which AI models are built and benchmarked. This narrative review examines, for a genetics and genomics readership, where AI-driven methods are already strengthening African molecular biology, where the supporting evidence remains preliminary, and what is required to translate technical capability into scientifically robust and equitable benefit. The central argument is that AI is especially consequential in African molecular biology, not simply because it automates analysis, but because it can help unlock insight from African genomic diversity, pathogen biology, and clinically relevant multi-omics data that remain underrepresented in global models. Across core molecular domains, AI is accelerating protein structure prediction, high-throughput variant calling and pan-genomic reference construction, genome-wide association analysis, transcriptomic interpretation, drug discovery, and CRISPR guide design. African initiatives such as H3Africa, the African Genome Variation Project, H3ABioNet, and the H3D Centre show that locally generated datasets and African-led computational pipelines can already support meaningful discovery, from improved variant interpretation to structure-guided therapeutic prioritization. At the same time, persistent barriers remain, including underrepresentation of African genomes in training data and reference genomes, uneven computational infrastructure, limited interdisciplinary training, fragmented governance, and the risk that AI-derived benefits will remain inaccessible to the populations whose data enable them. We conclude that the future impact of AI in African molecular biology will depend less on adopting global tools in the abstract and more on building African-led datasets, validation pipelines, governance frameworks, and translational pathways that make molecular discovery both scientifically robust and equitably useful. Looking ahead, the central perspective offered by this review is that Africa's exceptional genomic diversity should be treated as a scientific asset rather than an analytical liability: realising this will require population-representative pan-genome references, sustained computational capacity, and governance structures that ensure African populations are not only the source of the underlying data but also the principal beneficiaries of the discoveries it enables.

References

1. 1.Awuah, D., Hounkpe, A., Anane-Asamoah, J., Dakubo, W. K., Adu, I. K., Barnie, P. A., Ansah, E. O., Fosu, K., Aidoo, C. O., Essien, V., Adam, Y., Ninson, E., Quansah, R., & Kyei, F. (2026). The use of artificial intelligence in advancing molecular biology in Africa: a narrative review. Molecular Genetics and Genomics. https://doi.org/10.1038/s42003-026-09711-x