Data Quality and Data Integrity Considerations for Applying AI/ML as part of Process/Product Development By Bikash Chatterjee, President and CSO, Pharmatech Associates- A USP Company Abstract: As the integration of artificial intelligence (AI) and machine learning (ML) technologies gains traction as part of process and product development in the biologic industry ensuring data quality and integrity is paramount for successful implementation to not only having confidence in the process but also ensure the quality and integrity of any data used in development, that is generated and consumed. We will review some of the key challenges include data collection, preprocessing, validation, and governance, and provide insights as to how the industry is approaching these quality requirements
Learning Objectives:
Introduce participants to the primary steps in AI/ML model development
Understand when compliance considerations should be applied to be successful
Understand what approaches are being considered by industry
Understand what regulators looking for when applying an AI/Model to GMP applications
