Artificial Intelligence in Pharmaceutical Management Education: Opportunities, Challenges, and Impact

知识管理 业务 工程管理 数据科学 工程伦理学 工程类 计算机科学
作者
Puja Patel,D. Aruna Kumari,A. P. JAIN,Riya Gupta,Hemant Kumar Mishra
出处
期刊:International journal of pharmaceutical sciences and nanotechnology [BSP Books Private Limited]
卷期号:17 (6): 7697-7705 被引量:1
标识
DOI:10.37285/ijpsn.2024.17.6.6
摘要

Background: The use of Artificial intelligence (AI) is used nowadays rigorously in the pharmaceutical industry however, challenges remain in pharmaceutical management education, which prepares the professional who manages the industry. Through AI, the pharmaceutical industry designs drug discovery, formula development, marketing, strategies, quality assurance and many more. However, in literature, uses of AI in pharmaceutical management education have not been widely used and discussed with reference to India. Objective: This article explores the opportunity of key publication, its citation, gaps, and future scope of application of AI in the pharmaceutical industry as well as education that how AI could help professionals who pursue pharmaceutical management as a career in higher education. Moreover, the use of this paper will focus on how AI can facilitate pharmaceutical management education by adopting ethical guidelines and keeping scientific practices. Materials and Methods: To answer this, a systematic literature review with the SCOPUS database from 2013 to 2023 was conducted and selected 988 research articles out of 5,39,874 by applying the PRISMA approach. The keywords used to search the articles are pharmaceutical, education, artificial intelligence, marketing, strategy, future, business, management, accounting, pharmacology, toxicology, and pharmaceutics and trends. As an inclusion criterion, articles authored by Indian academicians in English languages were included. Result: The findings suggested that the role of AI in higher education is need of the hour as industries are looking for professionals with such skills. The study also concludes that AI in higher education could be used how to ensure customer preference through unstructured data for selecting the best segment, standardisation of products, regulatory approval, designing good research design in conducting clinical trials, strategies, and marketing. These identified topics could enrich the content of the pharmaceutical need and bring a revolution in the pharmaceutical industry for the betterment of society. Conclusion: Currently, pharmaceutical management education needs more professionals aligned with the uses of AI for developing strategies in marketing, branding, and product development. Further, it could be used to measure customer satisfaction and ethical regulations. Hence study recommends that curriculums need to be looked at from various angles.

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