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
Evaluating study plans effectively to strengthen vendor selection and enhance data management strategies
Aligning data management goals with vendor capabilities to reduce workload
Enhancing vendor selection though effective identification methods
Human governance of AI: principles, boundaries, and a human-in-the-loop model for clinical data decisions
Responsible adoption: how to train, measure, and scale without losing traceability (ATR: Audit Trail Review, RBQM: Risk-Based Quality Management, quality, and ethics)
Practical architecture: rules + ML + agentic AI for clinical data review—what to automate vs. what to keep human
Overviewing current approaches and trends within clinical data management
Assessing challenges and identifying solutions
Understanding the where the industry is heading to curb competition
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