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DIA 2027 · San Diego, CA

Track 3: Data, Technology and AI

This track focuses on harnessing the power of data, technology, and AI to transform drug development, regulatory decision-making, and patient access to therapies. Sessions explore innovations in data capture, curation, integration, and novel methods of evidence generation across the product lifecycle, from clinical trials to post-market settings, while addressing emerging challenges in governance, transparency, risk management, oversight, equity, and responsible use of clinical technologies, including digital health technologies and AI. While Track 10 addresses the frameworks and analytical strategies that transform those data into actionable evidence, this track focuses on the generation, curation, integration, and operational enablement of data. This track also highlights the role of real-world evidence, policy-driven data innovation, and AI-enabled insights to accelerate development, enhance regulatory submissions, and inform healthcare decision-making globally.

Themes
  • Driving Regulatory Acceptance of AI-driven Endpoints Beyond Traditional Endpoints: regulatory acceptance of AI-derived imaging endpoints, validation frameworks, and evidentiary requirements
  • Foundational Models and Multimodal AI for Drug Development: multimodal AI, generalizable imaging foundational models, and regulatory considerations for continuously evolving models
  • Towards Regulatory Acceptance of Digital Endpoints: balancing global health authority perspectives, lessons learned from agency interactions, documentation requirements, and evidence standards
  • Best Practices for AI Disclosure: insights into transparent disclosure to health authorities and patients on AI use to derive clinical insights, development clinical trial documentation, and submission-quality evidence
  • Governance of AI Implementations and Systems: Successful methods to foster innovation while balancing technological, safety, risk management, and data science standards within regulated and evolving spaces
  • Global, Harmonized Approaches Towards AI Applications in Drug Development: balancing global health authority expectations and driving reconciliation to power innovation
  • Data Standards and Interoperability: Driving multi-stakeholder collaboration through standardized data frameworks, ensuring quality, integrity, and cross-border data sharing
  • Regulatory Agency Digital Transformation: Strengthening Regulatory Systems Through Global Collaboration
Key Questions to be Addressed
  1. How can AI-generated evidence achieve regulatory acceptance in drug development and clinical research?
  2. How do we responsibly deploy advanced AI technologies, including foundation models and agentic AI, across the global drug development ecosystem?
  3. What governance, transparency, and trust frameworks are needed for AI in drug development and successfully regulatory interactions?
  4. How can data standards, interoperability, and global collaboration accelerate innovation while maintaining quality and compliance?
  5. How do we ensure that advancement in AI and novel technologies is accessible to all?
  6. How should regulators and industry validate, govern, and monitor advanced technology, such as generative AI and large language models, used in drug development and regulatory activities?
  7. What privacy-preserving and federated data-sharing approaches can support global collaboration while maintaining regulatory confidence in data quality, provenance, and auditability?

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