Preparing A Framework For Artificial Intelligence And Machine Learning Validation: A 3-Step Approach
We are thrilled to share the fascinating article penned by the brilliant trio – Ulrich Köllisch, Peter Baker, and Jennifer Roebber.
Their latest work, “Preparing A Framework For Artificial Intelligence And Machine Learning Validation: A 3-Step Approach,” delves into the fascinating world of AI/ML in the GXP domain. The article was published in Bioprocess Online.
The article sheds light on the challenges faced when introducing AI/ML into heavily regulated industries, such as GXP, where risk management and adherence to stringent processes are paramount. While AI/ML has made its mark in drug discovery. And certain pre-commercial GxP environments, it has not yet been fully embraced in GMP commercial settings.
The article proposes a proactive approach to prepare the industry for AI/ML adoption in GMP environments. It emphasizes the importance of implementing solid quality risk management (QRM) frameworks, establishing robust data governance programs, and developing internal AI/ML standards with quality oversight. By aligning with existing regulatory guidance and adopting AI/ML best practices, the article envisions the positive impact of AI/ML in drug manufacturing, promising reduced errors, recalls, and drug shortages.
As we eagerly await further developments, we’re already witnessing a transformation in the industry. With AI/ML paving the way for innovative solutions, the future looks bright in the pursuit of enhanced patient safety and improved pharmaceutical quality.
Read here the whole article: Preparing A Framework For Artificial Intelligence And Machine Learning Validation A 3-Step Approach (bioprocessonline.com)
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