Artificial Intelligence in Pharma: Positive Trends but More Investment Needed to Drive a Transformation

Peter V. Henstock
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引用次数: 6

Abstract

Over the past few years, pharmaceutical R&D has become aware of the potential benefits of leveraging artificial intelligence and its collective subfields including machine learning, deep learning, data science and advanced analytics. These technologies are being embraced across industries to provide enhanced automation, gain insights into data, and improve data-driven decision making. The evangelization from lower level technical experts has now been echoed by the top levels of many organizations, as exemplified by Vas Narasimhan’s (Novartis CEO) goal to evolve AI to place it at the “heart of the company” [1] and Alex Bourla’s (Pfizer CEO) aim to win the digital race in pharma using machine learning and AI to expedite R&D [2]. Although its value compared to pure science continues to be questioned, machine learning and particularly deep learning have introduced many compelling use cases.
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制药行业的人工智能:积极趋势,但需要更多的投资来推动转型
在过去的几年里,制药研发已经意识到利用人工智能及其集体子领域(包括机器学习、深度学习、数据科学和高级分析)的潜在好处。这些技术正在被各行各业所采用,以提供增强的自动化,获得对数据的洞察,并改进数据驱动的决策制定。来自低级技术专家的宣传现在已经得到了许多组织高层的响应,例如Vas Narasimhan(诺华公司首席执行官)的目标是发展人工智能,将其置于“公司的核心”[1],以及Alex Bourla(辉瑞公司首席执行官)的目标是利用机器学习和人工智能来加速研发,赢得制药行业的数字竞赛[2]。尽管与纯科学相比,机器学习的价值仍然受到质疑,但机器学习,尤其是深度学习,已经引入了许多引人注目的用例。
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