AI in Pharma
Chairs: Jeremy Wildfire, Sri Pavan Vemuri and Satish Murthy
See also: 2023 Discussion, 2024 Discussion
Current Use Cases
- Development Support: Chatbots, ADM, Adam TLF coding standards, general coding support and automation
- Specification Generation: Potential for generating specifications from SAPs and study documents to complete the code generation loop
- Clinical Operations:
- Improving operational efficiency
- Signal detection for safety monitoring
Implementation Strategy
- Break problems into manageable pipelines
- Start with proof-of-concepts (POCs)
- Leverage domain knowledge as a key asset
Technical Challenges
- Data Privacy: Major concern with handling sensitive clinical data securely
- Quality Control: Issues with traceability, repeatability, and quality control
- AI Limitations: Hallucination problems with AI outputs
- Implementation Issues: Tendency to over-engineer solutions rather than creating manageable ones
Organizational Barriers
- Management Resistance: Leadership not being on board due to established habits
- ROI Challenges: Difficulty demonstrating return on investment
- Validation Requirements: Need for repeatability and validation processes
- Training Needs: Comprehensive user training required for process changes
- Job Security Concerns: Worries about AI-related job displacement
Regulatory and Compliance Issues
- Audit Trail: Maintaining proper audit trails
- Documentation: Ensuring complete documentation and transparency of AI pathways
- Quality Standards: Meeting industry quality standards
- Data Governance: Implementing proper guardrails and controls on sensitive data