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

Dajiang Guo

doi:10.71222/k93eqd62

Authors

Dajiang Guo Deloitte China, Beijing, China Author

DOI:

https://doi.org/10.71222/k93eqd62

Keywords:

artificial intelligence, biomedical industry, drug discovery, clinical trials, efficiency improvement

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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