AI is accelerating digital transformation in life sciences. But implementing AI within a GxP-regulated environment introduces specific risks, especially when data is spread across multiple systems and processes.
Without the right foundations, organizations face challenges like inconsistent data, missing audit trails, and traceability gaps. At the same time, model transparency and explainability are increasingly scrutinized during audits. And because AI models evolve over time, a robust lifecycle management approach, aligned with CSA principles, becomes essential.
In this interactive webinar, QbD Group and delaware share how their combined expertise enables a pragmatic, integrated, and validated approach to implementing AI in regulated life sciences environments, so you can move from proof-of-concept to compliant deployment with confidence.
What you'll learn
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Best practices for AI implementation under GxP
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How to avoid common pitfalls, such as:
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Poor quality or insufficiently governed training/validation data
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Deploying black-box models with low explainability
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Applying insufficient governance and controls
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Insights into our joint framework methodology for compliant AI
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Real-world examples where innovation meets compliance