Mary Jo Lamberti1, Michael Wilkinson2, Bruce A Donzanti3, G Erich Wohlhieter4, Sudip Parikh5, Robert G Wilkins6, Ken Getz2. 1. Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA. Electronic address: mary_jo.lamberti@tufts.edu. 2. Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA. 3. Global Pharmacovigilance Innovation Policy, Genentech Inc, South San Francisco, CA, USA. 4. Digital Health and Innovation, Amgen, Thousand Oaks, CA, USA. 5. Drug Information Association of the Americas, Washington, DC, USA. 6. QPS Consulting LLC, Ashburn, VA, USA.
Abstract
PURPOSE: 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. METHODS: 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. FINDINGS: 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. IMPLICATIONS: 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.
PURPOSE: 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. METHODS: 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. FINDINGS: 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. IMPLICATIONS: 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.
Authors: Amy Abernethy; Laura Adams; Meredith Barrett; Christine Bechtel; Patricia Brennan; Atul Butte; Judith Faulkner; Elaine Fontaine; Stephen Friedhoff; John Halamka; Michael Howell; Kevin Johnson; Peter Long; Deven McGraw; Redonda Miller; Peter Lee; Jonathan Perlin; Donald Rucker; Lew Sandy; Lucia Savage; Lisa Stump; Paul Tang; Eric Topol; Reed Tuckson; Kristen Valdes Journal: NAM Perspect Date: 2022-06-27