DIA 2025 Global Annual Meeting

Component Type: Tutorial
CE: ACPE 2.75 Knowledge UAN: 0286-0000-25-505-L04-P; CME 2.75; IACET 2.75; RN 2.75

Pre-registration required and is an additional fee. *Please note: Short Courses are stand-alone events. Registration for the main conference, forum, etc. are not mandatory. Already registered? Log in to DIA's My Account My Events. This Short Course will be offered virtually – Join from anywhere! This short course will provide an overview of major regulations and regulatory frameworks of major world health authorities around the use of Artificial intelligence (AI)/GenAI in the life sciences industry. It will cover the ethical, legal, and practical aspects of AI implementation, focusing on compliance with current and emerging regulations. It will introduce concepts of the AI Risk Management Framework (RMF) by National Institute of Standards and Technology and explore how they can be extended to AI governance within the life sciences industry. It will provide some ideas on how to implement AI governance processes for the implementation of projects and ongoing maintenance. Receive $50 off your Global Annual Meeting total purchase by registering for two or more Short Courses. Purchases must be made at the same time to receive the discount. Discounts will be reflected on the last page of the cart. Registered attendees for this virtual Short Course will receive access to the course recording for 2 full months post-course! This allows you to remain flexible with your schedule and not worry if you need to step out momentarily. Have a conflict with the dates of the course, but are interested in the content? Register anyway and you will receive access to the recording!

Who should attend?

Professionals involved in the drug development and commercialization space (business as well as IT) who are interested in understanding the fast-growing discipline of AI regulations and governance frameworks, especially when AI/GenAI tools are being increasingly evaluated, prototyped and implemented in the life sciences industry.

Learning Objectives

• Discuss key regulations and regulatory frameworks around the use of AI in the life sciences industry
• Describe the business impact and compliance requirements for AI applications in drug development and commercialization
• Identify strategies to establish AI governance and compliance processes in the implementation of capabilities and ongoing maintenance

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-525-L04-P; CME 1.00; RN 1.00

Through this session, we will explore the advantages of incorporating AI and digital pathology for drug and biomarker discovery as part of a long-term companion diagnostic (CDx) partnership. We will also explore potential regulatory challenges in this ever-evolving space.

Learning Objectives

Discuss the importance of a complete development lifecycle of drug and companion diagnostic; Describe a complete development lifecycle of a drug and companion diagnostic; Describe potential regulatory challenges facing AI, Digital Pathology, and CDx development.

Chair

Greg Morehouse, MS

Speaker

Panelist
Brendan O'Leary

Panelist
Mark Stewart, PhD

Panelist
Elaine Katrivanos

Panelist
Andrew Ciupek, PhD

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-532-L04-P; CME 1.00; RN 1.00

This session will provide the transformative potential of Gen AI like ChatGPT. The session will explore the art of prompt engineering and the potential of LLM-based QnA system and will equip the attendees with the practical skills to harness the power of Gen AI.

Learning Objectives

Recognize Gen AI’s potential to enhance productivity; Discuss how to master prompt engineering techniques (zero-shot learning, few-shot learning, and chain of thought); Identify how to frame questions, analyze responses, and extract actionable insights more effectively.

Chair

Kevin Lee, MS

Speaker

Twenty Percent More Productivity with ChatGPT? Discover how Prompt Engineering is a Game Changer for Professional Development
Kevin Lee, MS

Harnessing Generative AI for Scientific Intelligence and Information Extraction
Ankush Chandna, MSc

Component Type: Forum
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-533-L04-P; CME 1.00; RN 1.00

Data science, AI and LLMs are redefining how we conduct clinical development to be faster, more efficient and more patient-centric. The National Insititute of Health, in partnership with the private sector, is leading the charge in driving change.

Learning Objectives

Describe examples of NIH innovation in tech-enabling clinical development through the application of data science, AI and LLMs; Identify potential opportunities for future public-private partnerships to accelerate tech enablement of clinical development.

Chair

Peter Ronco

Speaker

Panelist
Warren Kibbe, PhD, FACMI

Panelist
Susan Gregurick

Panelist
Kathleen Gavin, PhD, MS

Component Type: Forum
Level: Basic
CE: ACPE 1.00 Application UAN: 0286-0000-25-545-L04-P; CME 1.00; RN 1.00

Explore the transformative potential of generative AI (genAI) in Regulatory Affairs. This session will share insights and experiences from industry, focusing on education, culture change, and impactful use cases.

Learning Objectives

Recognize the value and potential of generative Artificial Intelligence (genAI) in Regulatory Affairs (RA); Outline how use cases are selected and implemented by Industry in their RA departments; Identify examples of application of genAI in RA; Discuss how to adapt and practice genAI within your own RA organization.

Chair

Paul Bolot, PharmD, MSc

Speaker

Introduction to genAI
Ben Moscovitch, MA, MBA

J&J Experience
Joseph Markmann, PhD, MBA

BMS Experience
Sandra Krogulski, MA

Novartis Experience
David Yao, PharmD

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-550-L04-P; CME 1.00; RN 1.00

We will discuss potential applications of AI in making diagnoses and treatment decisions, the appropriate ethical and regulatory boundaries, and how to progress forward balancing patient benefits against the potential risks.

Learning Objectives

Recognize the potential impact, ethical considerations, and evolving regulations related to the use of artificial intelligence in making diagnoses and treatment decisions; Discuss ways to formulate plans to bring artificial intelligence solutions to market and shape regulations.

Chair

Eli Weinberg, PhD, MS

Speaker

Panelist
Isaac Kohane, MD, PhD

Panelist
Shweta Maniar

Panelist
Subha Madhavan, PhD, FACMI

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 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

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-621-L04-P; CME 1.00; RN 1.00

Signal detection remains central to Pharmacovigilance activities. Better access and use of data, ever improving methods to maximise the utility of data, and better processes and governance to optimise signal detection activities are key focus areas as we continuously strive to do ever earlier and more effective safety signal detection. This session will discuss the current status of signal detection from all these aspects as well as how we anticipate the field evolving moving forwards.

Learning Objectives

Demonstrate and discuss how Generative AI can facilitate the future approach in augmenting PV systems in a trusted, compliant, and transparent manner; Summarize the opportunities and challenges with such an approach.

Chair

Andrew Bate, PhD, MA

Speaker

Industry Perspective
Mariette Boerstoel-Streefland, MD, MBA, MS

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-626-L04-P; CME 1.00; RN 1.00

Artificial Intelligence (AI) and Structured Content Authoring (SCA) tools are here. The challenge of seamlessly integrating these technologies into compliant and efficient workflows is reshaping the regulatory document development process. This session will delve into the balance and interdependence of four pillars (technology, process, people, and organization) to drive the business value of these tools. The interplay of these elements stokes an environment of thriving innovation, leading to enhanced efficiency, quality, and value proposition for the underlying business. AI/SCA have been thoroughly discussed in the regulatory writing process. Now there is focus on evolving technologies, the challenge lies in integrating these advancements into our workflows and processes, or implementing change management processes to allow for their use. Utilizing these tools coupled with organizational and process driven acceleration levers will condense the time to document approval and decrease the number of resources need to produce these documents. Moreover, the concerted and collaborative efforts across various functions, ranging from clinical sciences to biostatistics programming to review and approval pathways, ensures a thorough and compliant documentation process. It is time to focus and clearly define, emphasize, and promote the importance of the human element in the utilization of these tools, the people and the processes, at the heart of transformative authoring. Attendees will gain insights into the strategic integration of the four pillars in medical writing, drawing real-world use cases of these technologies in industry. These strategies, while transformative, do not necessitate a radical shift when applied independently. Instead, their true power lies in their harmonious application, which collectively refines traditional authoring paradigms.

Learning Objectives

Analyze the business value of AI and Structured Content Authoring tools in Regulatory Medical Writing to enhance efficiency and compliance; Evaluate process modifications in document development resulting from technology innovations to optimize workflow; Develop strategies to implement change management and organizational shifts that support technology-driven solutions.

Chair

David Meats, II

Speaker

Industry Perspective
James Best, MBA

Speakers
Louise Lind Skov, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-634-L04-P; CME 1.00; RN 1.00

Aftificial Intelligence (AI) is being adopted across the medicine lifecycle to increase insights into data and improve efficiency of processes, for the benefit of public and animal health. This session will address key elements to enable the safe and responsible use of AI.

Learning Objectives

Identify AI opportunities across the medicine lifecycle; Examine the legal ecosystem of AI across jurisdictions and identify opportunities for convergence; Discuss how regulators worldwide are developing guidance to support AI innovation in the medicine lifecycle; State other strategic AI initiatives, including approaches to leveraging AI with healthcare data.

Chair

Luis Pinheiro, PharmD, MSc

Speaker

Panelist
Timothe Menard, PharmD, MSc

Panelist
Tala Fakhouri, PhD, MPH

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-637-L04-P; CME 1.00; RN 1.00

This session will review the 3Rs (replacement, refinement, reduction) of animal use in non-clinical drug development research, and how new technologies including AI, Victorian Clinical Genetics Services (VCGs) and organoids are introducing innovative ways to meet these ethical principles.

Learning Objectives

Discuss the “3Rs” (replacement, refinement, reduction) of ethical animal use in non-clinical drug development research; Recognize how new technologies including AI, VCGs and organoids are being explored to follow these ethical principles as part of the overall drug development program.

Chair

Lindsay McNair, MD, MPH, MS

Speaker

How New Technologies are Being Developed to Align With Ethical Principles in Animal Research
Julie Frearson, PhD

Panelist
Szczepan Baran

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-641-L04-P; CME 1.00; RN 1.00

Industry leaders explore how biopharma is leveraging data foundations and technological advancements, including AI/ML, to improve access and representation in clinical trials, moving from DEI planning to tangible progress in clinical research.

Learning Objectives

Identify key areas where tech and analytics facilitate health equity advancement, focusing on achievable, tech-enabled outcomes; Illustrate concrete use cases where tech and data can support equitable clinical research, such as community outreach, recruitment, and measurement against diversity action plans; Discuss non-technical success factors needed to wholly support data-driven tech

Chair

Katrina Rice, MS

Speaker

From Data to Action: Driving Inclusive Clinical Trials
Del Smith, PhD, MS

Placing Patients First in the Era of Advanced Tech and AI
Patrick Gee, SR, PhD, MS

Responsible AI and Diversity in Clinical Trials
Nicole Ward, MBA, MS, PMP

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-652-L04-P; CME 1.00; RN 1.00

This session aims to explore the application of AI to drug labeling to enhance pharmacovigilance, reduce medication errors, and improve patient safety. Presenters will discuss the complexities and share real examples from both end-user and technology perspectives.

Learning Objectives

Discuss opportunities to apply artificial intelligence (AI) to drug labeling to enhance labeling review, surveillance activities, and medication error reduction; Identify the complexities and barriers to applying AI techniques to modernize labeling-related activities.

Chair

Daniele Sartori, MPharm

Speaker

Regulatory Perspective
Brett Howard, JD, PhD

From Text to Intelligence: Architecting AI Systems for Drug Labeling and Pharmacovigilance
Fatima Sabar, MSc

AI in Drug Labelling: Toward Harmonized, Globally Aligned Product Information
Daniele Sartori, MPharm

Computerized Labeling Assessment Using AI
Suranjan De, MBA, MS

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-655-L04-P; CME 1.00; RN 1.00

We’ve all heard the hype around emerging technologies and what it means for our industry, but when it comes down to adding value on the ground today, what advancements are actually making a difference in drug discovery, development, and distribution?

Learning Objectives

Describe and explain the advancements in data analytics and emerging technologies that are adding value to biopharma companies today.

Chair

Jessica Federer, MPH

Speaker

Panelist
Leo Barella

Panelist
Diogo Rao

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-666-L04-P; CME 1.00; RN 1.00

Regulatory, industry, and academic organizations discuss modern approaches to clinical drug development by describing novel methods and use cases integrating real-world data and AI to generate real-world evidence, with focus on oncology and rare diseases.

Learning Objectives

Discuss global regulatory guidance and frameworks supporting innovative designs using real-world data as well as approaches to inclusion of artificial intelligence (AI); Describe innovative approaches and methods through examples of applied AI use in drug development; Discuss areas of research interest and promote community collaboration.

Chair

Donna Rivera, PharmD, MSc, FISPE

Speaker

Applying Artificial Intelligence in Drug Development
Sid Jain, MBA

Use of Artificial Intelligence Approaches for Rare Diseases
Christina Mack, PhD, MPH

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-670-L04-P; CME 1.00; RN 1.00

The use of generative AI (genAI) is already shown to be useful in content generation. The use of genAI to accelerate development and maintainance of data standards and data transformation is rapidly evolving as it will be shown in this session.

Learning Objectives

Describe the current landscape of AI applications for developing, maintaining, and utilizing data standards to accelerate access to source data in electronic health records (EHRs) and registries; Evaluate how AI is currently shaping data standardization and transformation and anticipate its future impact on the landscape.

Chair

Jesper Kjær, MS

Speaker

Industry Feedback to AI in Data Standards
Robert DiCicco, PharmD

OHDSI OMOP AI and open science
Patrick Ryan

CDISC and AI
Chris Decker, MS

HL7 and the AI Train
Charles Jaffe, MD, PhD, FACMI, FACP

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-671-L04-P; CME 1.00; RN 1.00

This session explores the transformative potential of AI in pharma quality and compliance, sharing use cases, strategies, and regulatory insights. Attendees will learn how AI can transform quality and compliance in pharmaceutical companies.

Learning Objectives

Describe AI use cases in pharmaceutical quality management and compliance processes; Recognize the role, benefits, and risks of using AI in quality management; Discuss strategies for integrating AI technologies into existing pharmaceutical quality and compliance frameworks.

Chair

Hangyu (cedric) Liu, MS

Speaker

AI and the Quality Professional
Michael Pelosi, MA, MBA, MSc

AI in Pharma Quality and Compliance
Roshan D'Souza

Explainable Natural Language Processing: Why you see What you see
Meera Nagaria, MS