Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance
| dc.contributor.author | Lyimo, Beatus | |
| dc.date.accessioned | 2026-05-19T07:14:52Z | |
| dc.date.issued | 2026-02-28 | |
| dc.description | SDG 3: Good Health and Well-Being SDG 9: Industry, Innovation and Infrastructure | |
| dc.description.abstract | Africa continues to bear a disproportionate burden of infectious diseases, particularly antimicrobial-resistant (AMR) infections, which significantly affect public health and socio-economic development. Addressing these complex health threats requires innovative approaches to data analysis, pathogen surveillance, and intervention design. The emergence of advanced computational tools especially artificial intelligence (AI) is expected to reduce turnaround times for AMR prediction from days to hours by leveraging whole-genome sequencing (WGS)-based models. This article explores the synergistic integration of AI and bioinformatics, focusing on their application in combating AMR in Africa. It details how AI techniques, particularly machine learning (ML) and deep learning (DL) algorithms, can enhance genomic research by automating the analysis of large-scale sequence datasets, predicting resistance patterns, and modeling infections transmission dynamics. In regions with limited laboratory capacity, AI models can detect resistance genes rapidly and assist clinicians in selecting appropriate antibiotics, offering a faster and more scalable alternative to traditional diagnostics. Tools such as convolutional neural networks (CNNs) and support vector machines (SVMs) are examples of models capable of classifying pathogen strains based on genetic data. Furthermore, the article highlights the emerging role of large language models (LLMs) in supporting bioinformatics workflows. These tools aid researchers by generating analysis scripts, interpreting complex outputs, troubleshooting code errors, summarizing literature, and preparing manuscripts or grant proposals particularly benefiting early-career scientists who may lack access to advanced training or mentorship. Despite notable progress, significant challenges remain, including limited infrastructure, barriers to data sharing, and the urgent need for ethical guidelines and policies to govern AI integration. Ultimately, this article underscores the transformative potential of AI in advancing bioinformatics across Africa and advocates for sustained investment in infrastructure, capacity-building, and responsible policy frameworks to harness AI for improved health research and disease control outcomes. We propose 3 priority actions: building African AMR genomic datasets, investing in AI-ready infrastructure, and developing responsible data-governance frameworks. | |
| dc.identifier.uri | https://doi.org/10.1177/11779322261427123 | |
| dc.identifier.uri | https://dspace.nm-aist.ac.tz/handle/123456789/3818 | |
| dc.language.iso | en | |
| dc.publisher | SAGE Publications | |
| dc.subject | Artificial intelligence | |
| dc.subject | bioinformatics | |
| dc.subject | AMR | |
| dc.subject | genomic surveillance | |
| dc.subject | genomic AMR prediction | |
| dc.subject | AI-enabled diagnostics | |
| dc.title | Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance | |
| dc.type | Article |