Artificial Intelligence in Pharmaceutical Manufacturing: Enhancing Quality Control and Decision Making.

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Gonesh Chandra Saha, Lima Nasrin Eni, Hasi Saha, Prasanta Kumar Parida, Ravichandaran Rathinavelu, Sanjiv Kumar Jain, Barun Haldar


The convergence of Artificial Intelligence (AI) and pharmaceutical manufacturing signifies a pivotal juncture in the industry's evolution, offering unprecedented opportunities to bolster quality control and refine decision-making processes. This abstract provides a succinct overview of a comprehensive research paper that delves into the profound applications, benefits, challenges, and future trajectories of integrating AI into pharmaceutical manufacturing, with a specific focus on augmenting quality control measures and optimizing decision-making. Against the backdrop of stringent quality standards and regulatory demands inherent to pharmaceutical production, the infusion of AI technologies introduces a paradigm shift. This paper elucidates the manifold applications of AI, spotlighting its contributions to fortifying quality control mechanisms while empowering data-driven decisions. AI's impact on quality control is profound, notably through cutting-edge image recognition and computer vision systems. These innovations imbue visual inspection processes with unprecedented precision, detecting imperfections and anomalies that often elude human scrutiny. Augmented by real-time monitoring enabled by Internet of Things (IoT) devices, AI ensures continuous adherence to rigorous quality benchmarks, safeguarding product integrity. The transformative potential of AI extends to optimizing decision-making by harnessing the analytical prowess of data. By sifting through vast and complex datasets, AI empowers stakeholders with actionable insights, guiding strategic planning and resource allocation. Furthermore, AI's predictive capabilities are harnessed to forecast and mitigate risks stemming from supply chain dynamics, regulatory shifts, and quality deviations. In predicting batch release probabilities, AI accelerates the decision-making process, circumventing delays inherent to conventional manual testing. However, the adoption of AI in pharmaceutical manufacturing is not devoid of challenges. The efficacy of AI hinges upon the availability and quality of diverse datasets, underpinning accurate model training. Regulatory compliance poses another facetious hurdle, demanding that AI systems adhere to rigorous standards of validation, data fidelity, and transparency. Moreover, integrating AI into existing manufacturing frameworks necessitates meticulous planning to ensure a seamless transition devoid of operational disruptions. The paper explores the integration of Artificial Intelligence within pharmaceutical manufacturing, elucidating its profound contributions to elevating quality control measures and refining decision-making processes. By delving into various applications, challenges, and real-world instances, the paper offers insights into AI's transformative potential in propelling the pharmaceutical industry toward enhanced quality, compliance, and operational excellence.


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