A Conceptual Framework for Impact of Artificial Intelligence and Machine Learning (AIML) in Drug Development Within Pharmaceutical Industry

Mugdha Hemant Belsare () and Josip Burusic ()
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Mugdha Hemant Belsare: Swiss School of Business and Management
Josip Burusic: University of Zagreb

Chapter Chapter 17 in Innovation-Driven Business and Sustainability in the Tropics, 2023, pp 291-307 from Springer

Abstract: Abstract A conceptual framework is proposed within the scope of this study, which focuses on determining impact of AI in drug discoveries in pharmaceutical industry in India region. This is achieved by identifying internal and external determinants which are most significant to the success of the AI in Pharma. In the pharmaceutical sector, lowering prices and speeding up the development of new drugs has become a demanding and pressing issue. The pharmaceutical industry is expanding at a lightning speed in the pandemic time frame. The global pharmaceutical business is on the verge of a new paradigm, as rapid advances in artificial intelligence bring up the possibility of producing more effective drugs faster and at a lower cost. Artificial intelligence has the potential to revolutionize pharmaceutical industry as AI and new deep learning techniques have opened the pathway for modern drug development. Machine learning is a branch of AI that comprises the compilation of mathematical formulae and advanced statistics that researchers use through algorithms to solve real-world issues. AI has the potential to increase drug approval rates, lower development costs, accelerate the delivery of pharmaceuticals to patients and assist patients in complying to their treatment regimens. AI technology has a promising future in drug development, but the disparity across the domains is a significant blocking point, which this framework shall help to understand based on the internal and external determinants. This model is based on systematic literature review of most relevant ABDC journal publications. Developed conceptual framework will be helpful to the CEO, CXOs, business leaders and managers of the Pharma companies, while making strategic decision and action plan for execution. This shall help to gain the knowledge proactively acquired and deeply tested to implement and enjoy the results in making strong decisions. Using the most cutting-edge AI-based technologies not only shortens the time it takes for items to reach the market, but also raises product quality, ensures worker safety, maximises resource utilisation, and reduces production costs, which are the focus areas from the conceptual framework. Future research on confirming the conceptual framework, identifying most and least impactful factors, will involve formal collection and analysis of empirical data.

Keywords: Pharmaceuticals; Artificial Intelligence (AI); Machine Learning (ML); Drug discovery; Healthcare (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-2909-2_17

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DOI: 10.1007/978-981-99-2909-2_17

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