About the Consortium
The DIA Artificial Intelligence Consortium is a global, multi-stakeholder initiative uniting regulators, industry, academia, and technology innovators to tackle the practical challenges of AI in drug development. Organized into three working groups—Use Case Definition and Classification, Model Validation, and Regulatory Frameworks, Governance and Terminology Alignment—the consortium develops actionable frameworks, tools, and guidance.
In its first year, the consortium is focused on:
Defining a structured AI use case definition and classification framework
Creating practical AI/ML model validation guidance to evaluate quality, reliability, and fitness-for-purpose
Harmonizing regulatory terminology and frameworks to support cross-jurisdictional alignment
Why It Matters
This work benefits the life sciences community by establishing shared frameworks and standards, ensuring AI can be implemented safely, effectively, and responsibly.
Supporting regulatory and organizational decision-making grounded in validated evidence and structured AI classification frameworks.
Promoting harmonized global practices for AI in life sciences across regulators, regions, and disciplines to reduce friction and improve alignment.
Driving collaboration among regulators, industry, and academia to advance innovation and ultimately improve scientific rigor and patient outcomes.
Working Groups
The Consortium delivers coordinated outputs across three working groups.
Develops a structured, stage-based framework that categorizes AI use cases across the biopharma R&D–Manufacturing–Regulatory lifecycle.
Develops a practical checklist and reference framework covering performance metrics, data quality, reproducibility, and regulatory expectations to evaluate AI systems.
Harmonizes key definitions, terminology, and frameworks used by global regulators to improve clarity in regulatory submissions and cross-regional collaboration.
Key Activities and Status
From formation to publication — a structured pathway from expert insight to measurable global impact.
Establishing the consortium structure, onboarding of global members, and scoping of core workstreams. Working groups are formed around Use Cases, Model Validation, and Regulatory Terminology to define objectives and initial framework outlines.
Working groups build the core components of the AI Use Case Classification Framework, Model Validation & Valuation guidance, and harmonized regulatory terminology. Cross-group discussions ensure alignment across lifecycle stages, terminology, and evaluation considerations.
Working groups integrate concepts, refine frameworks, and harmonize terminology to support coherent cross-consortium outputs. Activities include peer review, stakeholder input sessions, and preparation for public dissemination.
Consortium outputs will be shared at the DIA Global Annual Meeting 2026, alongside publications and presentations through DIA channels to support global adoption and stakeholder engagement.
Participating Organizations
Representatives from regulators, industry, academia, and technology sectors working collaboratively toward shared frameworks for trustworthy AI in medicine.
Steering Committee & Project Leads
Governance emphasizes transparency, inclusivity, and the cross-sector collaboration required to responsibly advance AI innovation in regulated environments.
Christina Mack, Ph.D. is Senior Vice President of Applied AI Science within the AI and Technology Solutions (ATS) business, where she is responsible for driving scientific and technical innovation across the organization that has direct impact on patient health. Dr. Mack also leads the IQVIA Surveillance and Agile Analytics Team, which partners with biopharma and elite sports organizations such as NFL and NBA to prevent disease, reduce injuries and improve population health and safety.
In addition to this role, she serves as Chief Scientific Officer of IQVIA's Real World Evidence business, focused on applying advanced analytics and data science — including artificial intelligence — to improve population health, strengthen clinical evidence, and support regulatory decision-making.
Leon Rozenblit is a nationally recognized thought leader at the intersection of clinical research informatics and AI governance. As Co-Founder and Executive Committee member of the DCI Network at Beth Israel Deaconess Medical Center, Harvard Medical School, he has co-organized two AI governance conferences and published peer-reviewed research on multi-stakeholder approaches to responsible AI in healthcare.
A pioneer of "registry science," he has led dozens of national-scale registry programs and brings an interdisciplinary perspective spanning informatics, cognitive science, statistics, and law to bridge IT, scientific, and senior executive teams. He also advises health technology startups as Entrepreneur in Residence at Yale Ventures.
An influential and data-driven executive professional with a robust background in the Pharmaceutical and Public Health sectors, bringing a unique blend of expertise in leading digital transformation initiatives and leveraging data to guide corporations through complex business changes.
Recognized as a thought leader and industry expert in the data, digital health, and AI ecosystem, Sridevi excels at understanding industry trends and developing strategic perspectives to guide digital health and AI partnerships and investments. She brings high-level analytical skills and deep expertise in drug development, clinical, safety and regulatory processes, data management, digital innovation, and governance.
Stephanie Rosner serves as Scientific Program Manager for Artificial Intelligence at DIA, where she leads the development and coordination of the DIA AI Consortium. She works closely with co-chairs, working group leads, and member organizations to advance trustworthy AI frameworks across the life sciences industry.
Deliverables
Outputs will serve as foundational resources for organizations implementing AI in clinical, regulatory, and operational contexts.
AI use case classification resources that organize and clearly describe how AI is applied across research, development, manufacturing, and regulatory domains
Model validation and evaluation guidance to support consistent approaches for assessing reliability, appropriateness, and fit-for-purpose
Terminology and framework alignment tools that harmonize key concepts across regions, disciplines, and regulatory authorities
Cross-stakeholder publications and summaries that synthesize insights and recommendations
Benefits of Membership
Participation is open to partner organizations dedicated to advancing trustworthy AI in the life sciences.
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