Kodumuru Reshma, Sarkar Soumavo, Parepally Varun, Chandarana Jignesh
KBI Biopharma, Inc., Durham, NC 27704, USA.
Novartis AG, East Hanover, NJ 07936, USA.
Pharmaceutics. 2025 Feb 22;17(3):290. doi: 10.3390/pharmaceutics17030290.
The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation-continuously monitoring key manufacturing parameters. This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, within the pharmaceutical sector. Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. Yet, many challenges remain, including data integration and the ethical implications of AI-driven decisions, and most of all, regulatory compliance. This review also discusses recent trends, such as AI in drug discovery and blockchain for data traceability, with the intent to outline the future of autonomous pharmaceutical manufacturing. In the end, this review points to basic frameworks and applications that illustrate ways to overcome existing barriers to production with increased efficiency, personalization, and sustainability.
人工智能（AI）与物联网（IoTs）的整合是制药制造领域的一项重大进步，有效弥合了数字世界与物理世界之间的差距。通过将AI算法集成到物联网传感器中，生产过程和质量控制得到改善，从而提高了整体效率。这种整合有助于实现机器学习和深度学习，以进行实时分析、预测性维护和自动化——持续监测关键制造参数。本文回顾了在制药领域将AI与物联网与云计算和数据分析等关键使能技术协同整合的当前应用和潜在影响。本文讨论的应用侧重于工业预测分析和质量，并通过案例研究加以支撑，这些案例显示了产品质量的提高和停机时间的减少。然而，仍然存在许多挑战，包括数据整合以及AI驱动决策的伦理影响，最重要的是监管合规性。本综述还讨论了近期趋势，如药物发现中的AI和用于数据可追溯性的区块链，旨在勾勒自主制药制造的未来。最后，本综述指出了一些基本框架和应用，这些框架和应用说明了提高效率、个性化和可持续性来克服现有生产障碍的方法。
