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 will 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 data quality, interoperability, equity, and responsible use of digital health technologies. While Track 3 focuses on the generation, curation, integration, and operational enablement of data, Track 10 addresses the frameworks and analytical strategies that transform those data into actionable evidence. 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:
- Practical Applications of AI in Drug Development: Leveraging AI/ML for data generation, trial design optimization, predictive modeling, and regulatory submissions
- Real-World Evidence (RWE) for Development and Post-Market Use: Generating, curating, and integrating RWE to inform clinical development, regulatory decision-making, and health technology assessments (HTAs)
- Digital Health Technologies and Decentralized Trials: Lessons learned from DHT adoption, wearables, mobile apps, and approaches to scaling decentralized and hybrid trial solutions
- Data Standards and Interoperability: Driving multi-stakeholder collaboration through standardized data frameworks, ensuring quality, integrity, and cross-border data sharing
- Policy-Driven Innovation, Ethics, and Digital Equity: Understanding evolving policy frameworks for data use, ensuring information access and equity, and addressing cybersecurity, privacy, and responsible AI adoption
- Transformation of Translational Science through Data Integration: Leveraging big data and advanced analytics to accelerate translational research and connect preclinical and clinical insights
- Democratizing Patient Experience Data (PED) through AI and Digital Innovations: Exploring how advanced analytics, AI, and digital platforms can capture, democratize, and operationalize patient experience data to inform trial design, regulatory submissions, post-market decision-making, and equitable access to therapies
- Clinical Data Management and Enablement: Best practices and innovations in trial data capture, curation, cleaning, standardization, and integration to ensure high-quality datasets for regulatory submissions, RWE generation, and decision-making
Key Questions to be Addressed:
- How can AI and advanced analytics be practically applied to optimize clinical trial design, streamline development processes, and support regulatory decision-making?
- What best practices and lessons learned from digital health technologies (DHTs) and decentralized trial solutions can be scaled to improve patient access, engagement, and data quality?
- How can real-world evidence (RWE) and integrated data sources be effectively generated, curated, and applied in both development and post-market settings to inform regulatory submissions, safety monitoring, and healthcare decision-making?
- What data standards, interoperability frameworks, and policy-driven innovations are needed to ensure equitable information access, secure data sharing, and seamless collaboration across stakeholders globally?
