Article

The AI movement in life sciences

Name: Implement Consulting Group
Price range: $

Streamlining processes and enhancing quality

Published

8 November 2024

The life science industry is no stranger to the challenges of managing complex processes and maintaining stringent quality standards. Historically, these tasks have been document-heavy and time-consuming, often involving processes and systems that are not easily adaptable to new technologies. However, recent advances in AI have opened up new opportunities for streamlining these processes and improving not only the overall efficiency but also the quality of output.

The beginning of our AI journey in life sciences

At Implement, our journey with AI in the life science industry started with a focus on quality operations, recognising it as a document-heavy area with high data quality and with a high likelihood of overlapping processes described due to the manual processes required to create and maintain the documents. This decision quickly proved to be both fruitful and efficient, as the AI models delivered high-quality output from the initial iterations we had, e.g. on deviation processes and global QMS simplifications.

By targeting quality operations first, we were able to seamlessly integrate our learnings into discussions of improvements within the manufacturing processes, setting a strong foundation for further advances and AI assistance within the drug manufacturing value chains.

Rapid iteration and collaboration

A key factor in our AI projects in the life science industry has been the collaboration between industry experts and our AI specialists at Implement. By leveraging the specialised knowledge of subject matter experts (SMEs) from our clients and combining it with our business acumen and technical expertise, we have been able to create functioning AI concepts within just four to six weeks. Part of this rapid development cycle enables clients to review and utilise AI-generated output quickly, moving beyond proof of concept to real-world applications in their own processes much faster than what is traditionally seen in the industry.

When working with AI in heavily regulated processes and GxP, we encourage thinking across the various forms of AI and imagining a bit beyond the normal confines to ask “what is possible?” but also to start small and iterate fast.

One practical example that really stands out involved using AI to draft equipment validation protocols and functional requirement specifications, using only a template and the subject matter experts’ own text input. With this very minimal input, the AI was able to generate drafts that were 80-90% complete, demonstrating its remarkable capability to handle complex, context-specific tasks – all within two days.

Overcoming data challenges

One of the most impressive aspects of AI in life sciences is its ability to work with existing data straight out of the box. Unlike traditional complex machine learning projects that required extensive data cleaning, current generative AI models can process documents directly, significantly reducing preparation time while making more data available than the traditional “numbers only” thinking.

As an example, a benefit of the large language model-based AI system is the ability to read a large corpus of documents at scale. Instead of having humans read large quantities or being forced to access complex manufacturing systems for discrete data, much of this is now accessible to AI models.

The models are subsequently able to output this information in structured format for easy access by relevant subject matter experts with context and suggestions for how to proceed.

Advanced capabilities in manufacturing

AI is also making strides in more sophisticated areas of pharmaceutical manufacturing, such as biological processes and product formulation. Traditional methods such as Six Sigma have historically set high standards for process improvement, but AI now offers even greater advances.

By applying advanced algorithms, we are now seeing indications that AI can help achieve yield increases in manufacturing processes themselves as well as quality improvements in complex documentation processes, thus surpassing what was previously considered statistically feasible.

Call to action: pushing the boundaries

As we continue to explore the potential of AI in the life science industry, we invite other clients to join us in pushing the boundaries of this technology.

We encourage you to consider how AI could transform your processes and documentation, whether in quality assurance, regulatory, manufacturing or other areas. The possibilities are vast, and we are curious to see the further benefits as the technology develops.

An example of an AI architecture – retrieval augmented generation or RAG for short – often deployed in expertise areas such as QA, QC and RA. This type of solution is a proven method for how to infuse domain or business knowledge into an existing company GPT/AI solution.

Given our experience to date, it is becoming clear that value is present, also for early adopters who might go through more experiments before “getting it right”.

Conclusion

The integration of AI in the life science industry is no longer a distant vision but a present reality. By adopting AI early, companies can gain a competitive edge, improve operational efficiency and enhance product quality.

The collaboration between industry experts and AI specialists is driving rapid innovation, making this an exciting time for subject matter experts willing to embrace this technology.

As we continue to explore new applications and push the boundaries of what AI can achieve, we remain confident that the future of life sciences will be significantly shaped by these advances.

Reach out

For more insights and personalised guidance, reach out to us at Implement Consulting Group.

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