Artificial intelligence applied to local production of diagnostic tests
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Date
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Volume Title
Publisher
World Health Organization
Abstract
Description
3 p.
Strengthening local production of in vitro diagnostic medical devices is essential to improve access to timely, affordable and quality-assured diagnostics, particularly in low- and middle-income countries where health systems face structural constraints, and especially during health emergencies and pandemics. This perspective article examines the potential role of artificial intelligence (AI) in enhancing local production of diagnostic tests, analysing its applications across the value chain and lifecycle of in vitro diagnostics. The article describes how AI technologies, including machine learning, generative models, digital twins and robotics, can support research and development, product design, manufacturing processes, quality control, supply chain management and regulatory compliance. It highlights opportunities for improving efficiency, reducing costs, strengthening data integration and enabling real-time monitoring, while also identifying key barriers to adoption, such as limited technical capacity, high investment requirements, fragmented regulatory frameworks and challenges related to data quality and interoperability. Ethical considerations, including data privacy, transparency, bias and the need for human oversight, are also examined. The discussion emphasizes the importance of coordinated policy support, capacity-building and international collaboration to enable responsible AI integration, reducing reliance on imports and strengthening production autonomy. Intended for policy-makers, manufacturers and global health stakeholders, it outlines pathways to leverage AI for more resilient and equitable diagnostic production systems.
826
828
Strengthening local production of in vitro diagnostic medical devices is essential to improve access to timely, affordable and quality-assured diagnostics, particularly in low- and middle-income countries where health systems face structural constraints, and especially during health emergencies and pandemics. This perspective article examines the potential role of artificial intelligence (AI) in enhancing local production of diagnostic tests, analysing its applications across the value chain and lifecycle of in vitro diagnostics. The article describes how AI technologies, including machine learning, generative models, digital twins and robotics, can support research and development, product design, manufacturing processes, quality control, supply chain management and regulatory compliance. It highlights opportunities for improving efficiency, reducing costs, strengthening data integration and enabling real-time monitoring, while also identifying key barriers to adoption, such as limited technical capacity, high investment requirements, fragmented regulatory frameworks and challenges related to data quality and interoperability. Ethical considerations, including data privacy, transparency, bias and the need for human oversight, are also examined. The discussion emphasizes the importance of coordinated policy support, capacity-building and international collaboration to enable responsible AI integration, reducing reliance on imports and strengthening production autonomy. Intended for policy-makers, manufacturers and global health stakeholders, it outlines pathways to leverage AI for more resilient and equitable diagnostic production systems.
826
828
Keywords
Perspectives, Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Computer, Generative Artificial Intelligence, Computer Simulation, Internet of Things, Diagnostic Tests, Routine, Diagnostic Techniques and Procedures, Reagent Kits, Diagnostic, In Vitro Techniques, Biotechnology, Technology, Pharmaceutical, Capacity Building, Technology Transfer, Product Surveillance, Postmarketing, Legislation as Topic, Government Regulation, Ethics, Professional, Confidentiality