Phase 1: Foundational Work
Phase 1 ran from Q1 2025 through 2026, establishing the consortium's structure and its first frameworks. This phase is complete — this page is the historical record.
What Phase 1 Set Out to Do
Organized into three working groups, Phase 1 focused on establishing the frameworks that later phases would build on.
Defined a structured AI use case definition and classification framework
Created practical AI/ML model validation guidance to evaluate quality, reliability, and fitness-for-purpose
Harmonized regulatory terminology and frameworks to support cross-jurisdictional alignment
Working Groups
The Consortium delivered coordinated outputs across three working groups.
Developed a structured, stage-based framework that categorized AI use cases across the biopharma R&D–Manufacturing–Regulatory lifecycle.
Developed a practical checklist and reference framework covering performance metrics, data quality, reproducibility, and regulatory expectations to evaluate AI systems.
Converged 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 — the structured pathway Phase 1 followed.
Established the consortium structure, onboarded global members, and scoped core workstreams. Working groups were formed around Use Cases, Model Validation, and Regulatory Terminology to define objectives and initial framework outlines.
Working groups built the core components of the AI Use Case Classification Framework, Model Validation & Valuation guidance, and harmonized regulatory terminology. Cross-group discussions ensured alignment across lifecycle stages, terminology, and evaluation considerations.
Working groups integrated concepts, refined frameworks, and harmonized terminology to support coherent cross-consortium outputs. Activities included peer review, stakeholder input sessions, and preparation for public dissemination.
Consortium outputs were shared at the DIA Global Annual Meeting 2026, alongside publications and presentations through DIA channels to support global adoption and stakeholder engagement.
Consortium in Action
Moments from AI Consortium sessions and meetings at the DIA Global Annual Meeting.
Participating Organizations
Representatives from regulators, industry, academia, and technology sectors who shaped Phase 1.
Phase 1 Leadership
Governance emphasized 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 Senior 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 & Publications
Phase 1 outputs, available for download as they are published.
AI use case classification resources that organize and clearly describe how AI is applied across research, development, manufacturing, and regulatory domains
Link to publication coming soon
Model validation and evaluation guidance to support consistent approaches for assessing reliability, appropriateness, and fit-for-purpose
Link to publication coming soon
Terminology and framework alignment tools that harmonize key concepts across regions, disciplines, and regulatory authorities
AI CompendiumCross-stakeholder publications and summaries that synthesize insights and recommendations
Link to publication coming soon