DIA 2025 Global Annual Meeting

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-553-L04-P; CME 1.00; RN 1.00

Leveraging expertise from industry, this session will explore how responsible AI frameworks can maximize value in clinical research by ensuring ethical, transparent, and accountable AI use.

Learning Objectives

Recognize a set of responsible principles for AI in drug development; Demonstrate the ability to drive innovation and strategic advantage in AI-driven clinical trials; Propose how organizations can lead with integrity and trust in their AI applications.

Chair

Stephen Pyke, MSc

Speaker

Patient Perspectives on AI in Clinical Development
Alicia Staley, MBA, MS

Regulatory Perspectives on AI in Clinical Development
Tala Fakhouri, PhD, MPH

Industry Perspectives on AI in Clinical Development
Raja Shankar, MS

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-565-L04-P; CME 1.00; RN 1.00

This session will explore how Intelligent Automation (IA) and AI are transforming R&D and Regulatory Affairs by automating document-heavy processes and driving data-driven decisions. Through real-world use cases presented by industry sponsors, attendees will learn how IA and AI are being applied to improve efficiencies, reduce timelines, and optimize regulatory outcomes.

Learning Objectives

Discuss AI applications in drug discovery, clinical trials, regulatory, manufacturing, and pharmacovigilance; Explain practical use cases of AI in Regulatory Operations; Discuss advancing fit-for-purpose, risk-based frameworks for AI and global regulatory alignment.

Chair

Venkatraman Balasubramanian, PhD, MBA

Speaker

AI: Accelerating Clinical Trials Through Smarter Site Selection
Kevin Bugin, PhD, MS, RAC

Harnessing AI and Automation in Regulatory:  Insights from the Recent DIA RIM Intelligent Automation Survey
Cary Smithson, MBA

Industry Perspective
Cedric Berger, PhD, MBA

AI-Driven Drug Development: Transforming Life Sciences with Data and Innovation
Sridevi Nagarajan, PhD, MS, MSc

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-580-L04-P; CME 1.00; RN 1.00

As the potential of artificial intelligence (AI) is increasingly piquing the interest of researchers, it is critical to discuss how AI works by learning from the data, and how to leverage the results. Choosing the right approach for integrating AI into the research lifecycle can have a massive impact on likelihood of success. As one example, both generalized large language models (LLMs) and purpose build LLMs have their place in the toolkit needed to curate and analyze medical record and other patient data. This presentation will explore best practices/learnings for ensuring the right AI tool for the task, including LLMs tailored to the unique needs of pharmaceutical research; the impact for pharmaceutical research and for patients this can have; and imperatives for ascertaining the reliability and trustworthiness of AI solutions for medical research, as key to promoting adoption and responsible deployment in practice.

Learning Objectives

Identify key factors to consider when selecting AI strategies and methods for use in biomedical settings and pharmaceutical research; Describe the potential impact of AI strategies on research quality and patient outcomes, using real-world use cases and examples; Apply a framework to evaluate the suitability and readiness of AI technologies, such as large language models, for specific research or clinical applications.

Chair

Sarah Lyons, MSc

Speaker

Trust and adoption: ensuring the reliability of AI for Life Sciences applications
Patrick Loerch, PhD

The right AI tool for the task: real world learnings and patient impacts from deploying LLMs in healthcare
Sarah Lyons, MSc

Deploying Large Language Models in Clinical Research: Practical Considerations from Real World Examples
Troy Astorino

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-596-L04-P; CME 1.00; RN 1.00

This panel will explore available regulatory guidance for Real-World Data (RWD) quality assessments and how available implementing tools and frameworks may be applied for different types of RWD and use cases to help drug developers meet regulator expectations.

Learning Objectives

Recognize expectations for real-world data (RWD) quality assessments in studies intended for regulatory submissions; Identify available tools and frameworks for implementing RWD quality assessments; Explain ways to incorporate RWD quality assessments into internal workflows using practical approaches.

Chair

Eleanor Caplan, PharmD, PhD

Speaker

Regulatory Perspective
Luis Pinheiro, PharmD, MSc

Balancing Patient Centricity with Data Quality Requirements and Standards
Jennifer Farmer, MS

Learnings About Meeting RWD Fitness Standards From FDA Use Cases
Ulka B Campbell, PhD

Can we Separate Relevance From Reliability in Data Quality Assessments? Quality-by-Design in Secondary use of Health Data
Pamela Dobay, PhD, MSc