"The Impact of AI (Machine Learning and Automation) on Biopharmaceutical Manufacturing Industry" by Ruchi Sayal
This study investigates the transformative impact of artificial intelligence (AI), machine learning (ML), and automation on the biopharmaceutical manufacturing industry. By analyzing extensive literature and survey data, the research highlights how these technologies tackle challenges like data quality and complex biological systems, improve regulatory compliance, and enhance manufacturing efficiency. It identifies trends driving AI adoption, such as the need for process optimization, advancements in drug discovery, and better quality control. The study shows AI's disruption of traditional manufacturing models through real-time issue identification, enhanced quality control, and productivity boosts. Innovations like personalized medication, AI-powered robotics, and AI-assisted drug discovery underscore AI's transformative potential. The research concludes that AI adoption is rapidly advancing, driven by its ability to enhance efficiency, innovation, and competitiveness, and offers valuable insights for strategic decision-making and responsible integration of AI in biopharmaceutical manufacturing.
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