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美国信息技术与创新基金会(ITIF):2024人工智能赋能生物制药行业创新研究报告(中译版)(47页).pdf

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1、 itif.org Harnessing AI to Accelerate Innovation in the Biopharmaceutical Industry SANDRA BARBOSU|NOVEMBER 2024 AI has the potential to transform drug development by enhancing productivity across the entire development pipeline,boosting biopharmaceutical innovation,accelerating the delivery of new t

2、herapies,and fostering competition to help improve public health outcomes.KEY TAKEAWAYS Drug development is a costly,lengthy,and risky process,marked by declining productivity.Bringing a new drug to market can cost billions of dollars and take over a decade,with fewer than 8 percent of early candida

3、tes succeeding.AI has the potential to boost the efficiency of drug development,accelerating the delivery of new therapies and fostering competition,which could improve public health outcomes.AI applications span the entire drug development pipelinefrom speeding up drug discovery and enhancing clini

4、cal trials to streamlining manufacturing and supply chains.For broader,effective adoption of AI in drug development,key challenges such as data quality and availability,potential for algorithmic bias,workflow integration,and regulatory guidelines must be addressed.Key policy elements to advance AI a

5、doption include public funding for basic research,workforce training,support for privacy-enhancing data sharing and access,and a risk-based regulatory framework based on the risk level each AI use may pose to patients.Such policies are essential to unlocking AIs full potential in drug development,en

6、abling faster delivery of potentially life-saving therapies and expanding patient access.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 2 CONTENTS Key Takeaways.1 Introduction.3 The Drug Development Process.3 The Role of AI in Drug Development.7 Facilitating Drug Discovery.8 Streaml

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本文主要探讨了人工智能(AI)在生物制药行业中的作用,特别是在药物开发过程中的应用。文章指出,药物开发是一个成本高昂、耗时且风险大的过程,而AI的应用有望提高药物开发的效率,加速新疗法的上市,并促进竞争,从而改善公共健康成果。 关键点包括: 1. 药物开发过程包括药物发现、临床前测试、临床试验、监管审查和制造等多个阶段。 2. AI在药物开发中的应用包括加速药物发现、优化临床试验、改进制造和供应链等。 3. AI在药物发现阶段可以加速分析大数据,以识别有前景的化合物,减少发现新药物候选者的时间和成本。 4. 在临床试验阶段,AI可以优化患者选择,改善试验设计,加速数据分析,从而加快、更准确地得出结果。 5. AI还可以通过预测设备故障、提高资源分配效率等方式优化制造过程,从而提高生产效率和质量。 6. 尽管AI在药物开发中具有巨大潜力,但其广泛应用仍面临数据质量、算法偏见、工作流程整合和监管指南等挑战。 7. 为了促进AI在药物开发中的有效应用,政策制定者需要支持隐私增强的数据共享和访问,提供公共资金支持基础研究,加强教育和劳动力培训,并制定基于风险的监管框架。
人工智能如何加速药物研发? 人工智能如何优化临床试验设计? 人工智能如何提升制药生产效率?
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