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Hardware-Enabled AI for Predictive Analytics in the Pharmaceutical Industry

Author(s) Abhinav Balasubramanian, Niranjana Gurushankar
Country United States
Abstract The pharmaceutical industry is undergoing a transformation fueled by advancements in artificial intelligence (AI) and predictive analytics. This research paper investigates the crucial role of specialized hardware in enabling and accelerating AI-driven predictive analytics within the pharmaceutical industry. Moving beyond general-purpose, we explore the contributions of alternative hardware architectures such as Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), and neuromorphic computing. These technologies offer unique advantages in terms of power efficiency, performance, and customization for specific AI workloads. The paper examines how these hardware components facilitate the implementation of advanced software methodologies, including deep learning, machine learning, and natural language processing (NLP), for critical pharmaceutical applications. We analyze their impact on drug discovery, clinical trial optimization, personalized medicine, and supply chain management, highlighting real-world case studies and practical examples. The paper concludes by outlining future trends and research directions, emphasizing the transformative potential of hardware-enabled AI to revolutionize predictive analytics and drive innovation in the pharmaceutical industry.
Keywords Artificial intelligence, Predictive analytics, Clinical trials, Supply chain management, Big data, FPGA, ASIC, Neuromorphic computing, Hardware acceleration, Deep learning, Machine learning, Data mining, Predictive modeling, NLP
Field Engineering
Published In Volume 1, Issue 4, December 2020
Published On 2020-12-02
Cite This Hardware-Enabled AI for Predictive Analytics in the Pharmaceutical Industry - Abhinav Balasubramanian, Niranjana Gurushankar - IJLRP Volume 1, Issue 4, December 2020. DOI 10.5281/zenodo.14673310
DOI https://doi.org/10.5281/zenodo.14673310
Short DOI https://doi.org/g8z65s
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