DIA 2025 Global Annual Meeting

Component Type: Workshop
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-552-L04-P; CME 1.00; RN 1.00

Imagine a future where AI measures patient burden from provided inputs. In this interactive workshop, participants will design input logic for AI using mock data and protocols, then present their rationale to foster collaborative learning.

Learning Objectives

Define and categorize key factors affecting patient burden in clinical trials; Analyze patient-related data to design effective AI inputs; Evaluate and justify the role of human insight in enhancing AI-driven patient burden assessments for patient-centric trial outcomes.

Chair

Mihail Tanev, DMD, PhD

Speaker

Panelist
Karen Correa, PhD

From Measuring to Modeling the Patient Burden: An Interactive Take on What Defines the Patient Experience in the AI-Age
Mihail Tanev, DMD, PhD

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: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-555-L04-P; CME 1.00; RN 1.00

This session will explore the evaluation and implementation of assistive technology for clinical document content creation. The panelists will describe experimental design and adoption of solutions, cross-functional partnerships, and the impact to Medical Writing organizations.

Learning Objectives

Discuss and evaluate the potential benefits and challenges of implementing assistive technology, including both rule-based and AI-based solutions; Recognize the criticality of working with cross-functional teams to assess and implement technologies, ensuring successful adoption and return on investment; Discuss how AI can positively influence Medical Writing organizations.

Chair

Nancy Tam, MS

Speaker

Revolutionizing Medical Writing: Building the Assistive Technology Ecosystem
Matthew Renda, PhD, MS

Revolutionizing Medical Writing: Building the Assistive Technology Ecosystem
Aliza Nathoo

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-564-L04-P; CME 1.00; RN 1.00

This session will present multiple case studies illustrating the successful implementation of AI-driven RBQM solutions in real-world clinical trials. Attendees will gain insight into the practical applications of AI technologies.

Learning Objectives

Recognize the key benefits and challenges of applying AI technologies in RBQM; Identify insights into real-world examples of AI's impact on clinical trial risk management; Discuss strategies for integrating AI into existing RBQM frameworks to improve decision-making and compliance; Describe how AI enhances collaboration across clinical trial stakeholders and supports more proactive risk-based strategies.

Chair

Artem Andrianov, PhD, MBA

Speaker

Panel Discussion: AI Teammates in Trials - Elevating Risk Management and Human Insight
Brian Barnes, MA

Novo Nordisk’s Path to Integrated Quality and Risk Management with AI-Powered Study Builder
Bo Maach-Møller, MPharm

Unlocking AI Potential In RBQM: Today and Tomorrow
Cheng Su, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-572-L04-P; CME 1.00; RN 1.00

This session will explore engagement pathways with global regulators to obtain feedback on AI technologies in drug development. It will also identify effective evaluation strategies to support regulatory acceptance of novel methodologies.

Learning Objectives

Identify the appropriate regulatory pathways and mechanisms for engaging with health authorities to discuss AI technologies in drug development applications; Discuss regulatory strategies, use cases, and considerations for AI model development based on context of use; Evaluate lessons learned and experiences from manufacturers, developers and health authorities.

Chair

Ryan Hoshi, PhD, MBA, MSc

Speaker

EMA Perspectives on the Use of AI in the Lifecycle of Medicines
Luis Pinheiro, PharmD, MSc

AI and Machine Learning in Pharmaceutical Manufacturing
Gert Thurau, DrSc, PhD

FDA Perspectives on the Use of AI to Support Regulatory Decision-Making for Drug and Biological Products
Tala Fakhouri, PhD, MPH

Technical and Regulatory Considerations for Streamlining Drug Development with Digital Twins: Sample-size Reduction Case Study
Aaron Smith, PhD

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 Michael 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: Forum
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-581-L04-P; CME 1.00; RN 1.00

Tufts CSDD and DIA collaborated on a research study with a working group of 16 biopharmaceutical companies and CROs examining the current use of artificial intelligence (AI) and machine learning (ML) to support clinical research.

Learning Objectives

Describe the level of adoption of artificial intelligence (AI) and machine learning (ML) across biopharmaceutical companies and contract research organizations and the top activities where AI/ML is being implemented and utilized; Discuss the impact of AI/ ML on time savings, investment, and deployment of organizational resources.

Chair

Mary Jo Lamberti, PhD, MA

Speaker

Panelist
Mukul Virmani, Esq, MS

Use Case Examples within Clinical Development
Emily Carter, MBA

Organizational Use Case
Luciana Petcu, MS

Component Type: Forum
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-595-L04-P; CME 1.00; RN 1.00

We present trends and insights from an analysis of participant payment language of more than 7,500 consent forms reviewed by a large, independent IRB and discuss the ethical implications of insufficient compensation for trial participants.

Learning Objectives

Explain trends and insights in clinical trial participant payments based on 7500 clinical trial ICFs collected and analyzed between 2019 and 2024; Examine the language used to describe payments and how that impacts participants; Discuss ethical issues and assumptions about IRB approval of participant compensation for research.

Chair

Kelly Fitzgerald, PhD

Speaker

Panelist
Kelly Fitzgerald, PhD

Panelist
Donna Libretti Cooke, JD