- IOT and Technology Evolution
- From Reactivity to Proactively saving shipments.
- AI in predictability – The Next frontier
- Design and implementation of an automated Data Management pipeline that standardizes key processes and reduces manual workload across studies
- Lessons learned from developing and deploying the Data Management pipeline across multiple clinical studies
- How automation and reproducible pipelines are shaping the future of Data Management and Clinical Data Science
- Unique Nature of Radiopharmaceuticals in Clinical Trials
- Strategies for Effective Data Management
- Operational Challenges in Trial Design & Execution
- Complexity of Multimodal Data Integration
- Unique Challenges in Data Management
- Key considerations for delivery of a large portfolio
- Challenges and opportunities in the implementation of a new delivery model/framework
- A case study: Internal Vs External Resourcing
- Vendor Relationships in a Changing Environment
- Best Practices for Successful Vendor Collaboration
- From Vendor to Partner
- Keys to Establish Effective Oversight
- Understanding challenges faced when integrating AI into clinical data management processes
- Evaluating AI readiness and identifying red flags during development, implementation and deployment
- Taking away actionable insight how to use and incorporate AI, by learning from setbacks and successes
