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Analysis of Efficiency Improvement Path Scheme in Biomedical Industry Driven by AI

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  • Guo, Dajiang

Abstract

The biomedical industry faces persistent efficiency challenges, including prolonged R&D cycles, high development costs, complex clinical trials, and fragmented data management. Driven by advances in artificial intelligence (AI), novel solutions are emerging to address these bottlenecks across the drug discovery, clinical, manufacturing, and knowledge management domains. This review systematically analyzes AI-driven efficiency improvement pathways, highlighting accelerated drug discovery, optimized clinical trials, intelligent manufacturing and supply chain, and data-driven decision support. Key challenges, such as data quality, regulatory constraints, system integration, and talent gaps, are discussed, alongside potential future developments in self-supervised learning and generative models. The study emphasizes the transformative potential of AI to enhance productivity, reduce costs, and support informed decision-making, offering strategic insights for enterprises seeking sustainable innovation in the biomedical sector.

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  • Guo, Dajiang, 2025. "Analysis of Efficiency Improvement Path Scheme in Biomedical Industry Driven by AI," European Journal of AI, Computing & Informatics, Pinnacle Academic Press, vol. 1(3), pages 10-18.
    • Handle: RePEc:dba:ejacia:v:1:y:2025:i:3:p:10-18
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