Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers

Abstract

Artificial intelligence (AI) has a supplementary role in clinical toxicology in Africa, addressing key challenges such as delayed diagnoses, limited expertise, and inadequate healthcare infrastructure. This method has the potential to increase diagnostic accuracy, optimize treatment strategies, and advance research on toxic substance exposure and poisoning cases. AI-driven tools, including machine learning algorithms and decision-support systems, enhance the early detection and risk assessment of toxicities. AI-powered predictive models facilitate precision medicine by designing treatment plans for individual patient profiles. Integrating this in telemedicine expands access to toxicology expertise, particularly in resource-limited settings. Additionally, AI accelerates research by analyzing large datasets, identifying trends, and predicting toxicological risks, thus contributing to public health interventions. Despite these advancements, challenges such as data poverty, ethical issues, and restrictive policies hinder its full potential in African healthcare. These gaps can be bridged through policy reforms, capacity-building initiatives, and robust AI frameworks, which will be crucial in maximizing AI benefits for clinical toxicology. This narrative review highlights the emerging applications of AI in Africa, emphasizing the need for collaborative efforts to ensure equitable and effective implementation. However, its adoption is limited by financial constraints, scarce datasets, weak infrastructure, and ethical concerns.

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