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Ilmu Komputer & AI editorial

Open AccessOA2026

Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance

A structured digest of B. Lyimo's 2026 review on AI-driven genomic surveillance, diagnostic acceleration, and responsible data governance for AMR in Africa

B. Lyimo· Bioinformatics and Biology Insights· 2026· DOI 10.1177/11779322261427123

The core problem

Africa bears a disproportionate burden of infectious diseases, with antimicrobial-resistant (AMR) infections significantly impacting public health and socio-economic development. Traditional laboratory-based surveillance and diagnostics are often too slow and resource-intensive to meet the continent's needs. The emergence of advanced computational tools, especially artificial intelligence (AI), offers a promising path forward. By leveraging whole-genome sequencing (WGS)-based models, AI is expected to reduce turnaround times for AMR prediction from days to hours. This article explores the synergistic integration of AI and bioinformatics for combating AMR in Africa, detailing how machine learning (ML) and deep learning (DL) can enhance genomic research, automate large-scale sequence analysis, predict resistance patterns, and model transmission dynamics. It also addresses the challenges of limited infrastructure, data sharing barriers, and the urgent need for ethical guidelines. The central thesis is that sustained investment in infrastructure, capacity-building, and responsible policy frameworks is essential to harness AI for improved health research and disease control outcomes acr

Innovation

The review finds that AI techniques can significantly enhance genomic research by automating the analysis of large-scale sequence datasets, predicting resistance patterns, and modeling infection transmission dynamics. In regions with limited laboratory capacity, AI models can rapidly detect resistance genes and assist clinicians in selecting appropriate antibiotics, offering a faster and more scalable alternative to traditional diagnostics. For example, CNNs and SVMs have been successfully applied to classify pathogen strains based on genetic data, enabling high-throughput surveillance. The integration of AI with WGS-based models is projected to reduce AMR prediction turnaround from days to hours. Furthermore, LLMs are emerging as valuable tools for supporting bioinformatics workflows, particularly for early-career scientists who may lack access to advanced training or mentorship. These tools aid in generating analysis scripts, interpreting complex outputs, troubleshooting code errors, summarizing literature, and preparing manuscripts or grant proposals. Despite these advances, the review identifies persistent challenges: limited computational and laboratory infrastructure, barrier

Africa bears a disproportionate burden of infectious diseases, with antimicrobial-resistant (AMR) infections significantly impacting public health and socio-economic development. Traditional laboratory-based surveillance and diagnostics are often too slow and resource-intensive to meet the continent's needs. The emergence of advanced computational tools, especially artificial intelligence (AI), offers a promising path forward. By leveraging whole-genome sequencing (WGS)-based models, AI is expected to reduce turnaround times for AMR prediction from days to hours. This article explores the synergistic integration of AI and bioinformatics for combating AMR in Africa, detailing how machine learning (ML) and deep learning (DL) can enhance genomic research, automate large-scale sequence analysis, predict resistance patterns, and model transmission dynamics. It also addresses the challenges of limited infrastructure, data sharing barriers, and the urgent need for ethical guidelines. The central thesis is that sustained investment in infrastructure, capacity-building, and responsible policy frameworks is essential to harness AI for improved health research and disease control outcomes across Africa.

This article is a narrative review synthesizing current literature and technological developments at the intersection of AI, bioinformatics, and AMR in Africa. The methodology involved a structured search and analysis of peer-reviewed studies, technical reports, and policy documents focusing on AI applications in genomic surveillance, diagnostic prediction, and clinical decision support. Key AI techniques examined include machine learning algorithms such as support vector machines (SVMs) and deep learning architectures like convolutional neural networks (CNNs), which are capable of classifying pathogen strains from genetic data. The review also assesses the emerging role of large language models (LLMs) in bioinformatics workflows, including script generation, output interpretation, and literature summarization. The analysis is contextualized within Africa's unique infrastructure and capacity constraints, drawing on examples of AI deployment in low-resource settings. The synthesis culminates in three priority actions: building African AMR genomic datasets, investing in AI-ready infrastructure, and developing responsible data-governance frameworks. No primary data collection or experimental validation was conducted; the findings are based on existing evidence and expert interpretation.

Why it matters

The findings underscore the transformative potential of AI in advancing bioinformatics across Africa, but also highlight the need for a context-sensitive approach. The synergy between AI and bioinformatics can be conceptualized as a pipeline: from sample collection and WGS to AI-driven analysis and clinical decision support. This flow can be represented as: Mathematically, the prediction of resistance can be framed as a classification problem where a model f maps genomic features x to a resistance label y : y = f (x; θ), with parameters θ learned from data. The performance of such models depends on the quality and diversity of training data, which is currently limited for African pathogen strains. Therefore, building African AMR genomic datasets is critical to avoid biased predictions. Investing in AI-ready infrastructure—such as cloud computing, high-performance clusters, and reliable internet—is equally important. Ethical considerations include data privacy, informed consent, equitable benefit-sharing, and avoiding algorithmic bias. Responsible data-governance frameworks must be co-developed with African stakeholders to ensure that AI integration respects local values and regulations. The review concludes that without sustained investment and policy support, the potential of AI to combat AMR in Africa will remain unrealized.

Who should read this

CS practitioners and researchers

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