WASHINGTON — Jan. 2024 — DIA, a global organization of life sciences professionals, life science professionals, and the Tufts Center for the Study of Drug Development (Tufts CSDD) have come together to conduct a study that furthers the understanding of how artificial intelligence (AI) supports the continuum of drug development.

The study is supported by 16 leading biopharmaceutical companies and contract research organizations (CROs). This working group will examine the use of AI, including machine learning (ML) and natural language processing (NLP) models, in clinical operations and development, in areas such as site identification, patient recruitment, pharmacovigilance, quality assurance, and clinical monitoring, with the goal of recognizing the most effective use of AI tools.

"We want organizations around the world to understand how AI is being applied in drug development and how they can get the most out of their investment in this technology," said Marwan Fathallah, President and Global Chief Executive of DIA. "Many are putting a lot of time, money, and resources into AI but aren't seeing the return on investment that they expect. By identifying how their peers successfully implement these tools into their workflows, we can spur industry-wide development that makes promising new treatments available to the patients who need them faster than ever before."

The working group will provide input into the study methodology, including the development of a landscape survey and participation in in-depth interviews that will highlight AI use and implementation. Results will be published in a peer-reviewed article.

"AI adoption has matured since our earlier study and we will examine which approaches are most effective and impactful within clinical development," said Dr. Mary Jo Lamberti, Research Associate Professor and Director of Sponsored Research at Tufts CSDD. “This study represents an immense opportunity to benchmark the use of AI across the industry.”

DIA and Tufts CSDD previously partnered in 2019 for a landscape analysis of how AI was used in biopharmaceutical development. That study revealed that AI was utilized most often in patient selection and recruitment. It also identified that a lack of adequate staff with appropriate skill sets was a significant hurdle that hindered its widespread adoption.

For more information on participating in the study, please reach out to science@diaglobal.org.