Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa

Le résumé fourni par la source

Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and projections suggest this could rise to 10 million annually by 2050 without effective interventions. Sub-Saharan Africa (SSA) faces considerable challenges in managing AMR due to insufficient surveillance systems, resulting in fragmented data. Technological advancements, notably artificial intelligence (AI), offer promising avenues to enhance AMR biosurveillance. AI can improve the detection, tracking and prediction of resistant strains through advanced machine learning and deep learning algorithms, which analyze large datasets to identify resistance patterns and develop predictive models. AI's role in genomic analysis can pinpoint genetic markers and AMR determinants, aiding in precise treatment strategies. Despite the potential, SSA's implementation of AI in AMR surveillance is hindered by data scarcity, infrastructural limitations and ethical concerns. This review explores what is known about the integration and applicability of AI-enhanced biosurveillance methodologies in SSA, emphasizing the need for comprehensive data collection, interdisciplinary collaboration and the establishment of ethical frameworks. By leveraging AI, SSA can significantly enhance its AMR surveillance capabilities, ultimately improving public health outcomes.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.