As AI becomes embedded in high-stakes drug development processes, trust and reliability are becoming essential for adoption. This is especially true in clinical and regulatory workflows, where AI models must be robust, reproducible, transparent, and fit for purpose. Challenges are emerging around data provenance, bias, model validation, performance benchmarking, and documentation. Attendees can learn:

• What an AI credibility assessment framework looks like in regulated drug development environments
• How to validate and verify AI models for robustness, reproducibility, and transparency
• Where data provenance, bias assessment, and performance benchmarking fit into model credibility
• How credibility frameworks can support faster, safer integration of AI into clinical and regulatory workflows while maintaining inspection readiness