Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA.
Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA.
Clin Ther. 2019 Aug;41(8):1414-1426. doi: 10.1016/j.clinthera.2019.05.018. Epub 2019 Jun 24.
The Tufts Center for the Study of Drug Development (CSDD) and the Drug Information Association (DIA) in collaboration with 8 pharmaceutical and biotechnology companies conducted a study examining the adoption and effect of artificial intelligence (AI), such as machine learning, on drug development. The study was conducted to clarify and understand AI adoption across the industry and to gather detailed insights into the spectrum of activities included in the definition of AI. The study investigated and identified analytical platforms and innovations across pharmaceutical and biotechnology companies currently being used or planned for in the future.
A 2-part method was used that comprised in-depth interviews with AI industry experts and a global survey conducted across pharmaceutical and biotechnology organizations. Eleven in-depth interviews focused on use and implementation of AI across drug development. The survey assessed use of AI and included perceptions about current and future use. The survey also examined technology definitions, assessment of organizational and personal AI expertise, and use of partnerships. A total of 402 responses, including data from 217 unique organizations, were analyzed.
Although 7 in 10 respondents reported using AI in some capacity, a wide range of use was reported by AI type. Patient selection and recruitment for clinical studies was the most commonly reported AI activity, with 34 respondents currently using AI for this activity. In addition, identification of medicinal products data gathering was the top activity being piloted or in the planning stages, reported by 49 respondents. The study also revealed that the most significant challenges to AI implementation included staff skills (55%), data structure (52%), and budgets (49%). Nearly 60% of respondents noted planned increases in staff within 1-2 years to support AI use or implementation.
Despite the challenges to AI implementation, the survey revealed that most organizations use AI in some capacity and that it is important to the success of an organization's workforce. Many organizations reported expectations for increasing staff as implementation of AI expands. Further research should examine the changing development landscape as the role of AI evolves.
塔夫茨药物开发研究中心（CSDD）与药物信息协会（DIA）合作，与 8 家制药和生物技术公司一起开展了一项研究，考察人工智能（AI），如机器学习，在药物开发中的采用和影响。该研究旨在阐明和了解整个行业的 AI 采用情况，并深入了解 AI 定义所包含的活动范围。该研究调查并确定了制药和生物技术公司目前正在使用或计划在未来使用的分析平台和创新。
采用了两部分方法，包括对 AI 行业专家的深入访谈和对制药和生物技术组织的全球调查。11 次深入访谈集中在药物开发中 AI 的使用和实施上。该调查评估了 AI 的使用情况，并包括对当前和未来使用的看法。该调查还审查了技术定义、对组织和个人 AI 专业知识的评估，以及合作伙伴关系的使用情况。共分析了 402 份回复，包括 217 个独特组织的数据。
尽管 70%的受访者报告在某种程度上使用了 AI，但 AI 类型的使用范围很广。患者选择和招募参加临床试验是最常见的 AI 活动，有 34 名受访者目前正在使用 AI 进行这项活动。此外，识别药品数据收集是正在试点或计划阶段的最高活动，有 49 名受访者报告了这一活动。该研究还表明，AI 实施的最大挑战包括员工技能（55%）、数据结构（52%）和预算（49%）。近 60%的受访者表示，计划在 1-2 年内增加员工，以支持 AI 的使用或实施。
尽管 AI 实施面临挑战，但调查显示，大多数组织在某种程度上使用 AI，这对组织员工的成功至关重要。许多组织报告称，随着 AI 的实施扩大，他们期望增加员工。进一步的研究应该研究随着 AI 角色的演变，不断变化的发展格局。
