Validation 4.0: How AI Is Transforming Life Sciences Quality Management

Many life sciences companies have invested in the digitalization of their validation processes. According to a recent survey by the International Society of Pharmaceutical Engineering (ISPE), 74% of participants expect to implement a DVT by 2024.¹  These DVTs enable faster documentation generation and integration with other critical systems, such as quality management systems (QMS), change control systems, laboratory information management systems (LIMS), manufacturing execution systems (MES), and data lakes containing CPP data. Using application programming interface (API) technology, which many of these systems support as they are SaaS-based, allows seamless integration between platforms.

This capability empowers organizations to transfer data between systems more efficiently and retrieve critical validation information in real time. For example, instead of waiting for a paper-based lab report on critical quality attributes (CQAs) for a process validation report, the system can automatically pull the approved results directly from an integrated LIMS in real time.

A key use case for AI in quality management is the efficient production of technical documents, such as validation protocols. This is achieved using a large language model (LLM) trained specifically to generate validation documentation in accordance with an organization’s standards and requirements. When paired with a retrieval-augmented generation (RAG) AI system, the LLM can ingest technical details from sources like user requirement specifications and equipment specifications to develop validation tests. Essentially, the RAG functions like a validation engineer, interpreting requirements, aligning them with equipment design, and creating test scenarios to verify if the equipment meets the required specifications.

The accuracy of the AI-generated output is verified by an operator who reviews and approves the test documents following existing paper-based workflows, which minimizes risk. Privacy concerns related to using an open AI model are mitigated by deploying the model on secure, private servers, ensuring controlled access and safeguarding data privacy.

In the future, combining DVTs with ML will unlock even greater automation opportunities. Imagine a scenario where a change to a user requirement affects the control of a CPP. In this case, AI technologies could automatically generate a test document to assess the effectiveness of that change. Once the document is approved, testing can proceed more efficiently by pulling relevant evidence from systems interfaced with the DVT, seamlessly integrating the results into the validation report.

This is a crucial step toward the ultimate goal of Validation 4.0: real-time verification.

Other valuable use cases for AI in quality management can be found in quality control (QC) on medical device production lines, where vision systems inspect for defects. These systems are trained to recognize defects using a library of images, but it is difficult to simulate every possible defect. AI and ML can enhance these systems by predicting new, unseen defects and flagging them for operator review. The operator can then use parallel QC methods to verify whether it is truly a defect. This feedback loop allows the AI model to learn and improve, reducing false rejects and increasing overall product quality.

This approach not only strengthens medical device validation but also ensures the accuracy of AI/ML outputs, further improving the quality assurance process.