Since September 2023, No deviation has been collaborating with Arc53, aimed at developing alternatives to ChatGPT for use in the pharmaceutical industry, ensuring that the security of internal data remains uncompromised.

AI integrations are becoming increasingly common, with almost weekly announcements of new startups claiming their AI models are ready for production. Yet, beyond the initial excitement, the reality paints a different picture.

Few AI solutions are truly ready for real-world application, and most rely on sending data to third-parties. The challenges often cited include the risk of data leaks, the generation of low-quality AI outputs, and prohibitive costs. The truth is, there is no ready-made solution that delivers on all of AI’s promises right out of the box.

Adopting a realistic approach towards AI integration is becoming increasingly popular. This strategy moves away from expecting AI to serve as an all-knowing oracle. Instead, it capitalizes on utilizing your existing documents as the primary data source. Enter the innovative Retriever-Augmented Generation (RAG) system, an AI-driven search engine designed to streamline information retrieval.

It efficiently sifts through your documents to find and present the information you’re searching for in a user-friendly manner, directly answering your specific queries. Moreover, each answer is traceable back to its original document, ensuring verifiability of the response’s accuracy and authenticity. This method is continuously improved through precise fine-tuning and a constructive feedback loop, meeting and exceeding production-level precision demands. Its lower processing demands mean this system can be efficiently managed in-house, offering heightened security and reliability by operating on-premises, free from external dependencies.

Partnering with Arc53, we’ve witnessed the successful large-scale deployment of this solution, now fully operational. We’re eager to share the invaluable insights gained from integrating AI seamlessly into organizational systems.

How do you ensure data security with AI?

• On-premise deployment only: Given AI models’ capacity to process vast data volumes and their significant benefits in doing so, the risk of third-party access abuse On-premise deployment remains the safest method to secure your solution for the future.
• No external connections: This ensures stability and complete version control. Reliance on API connections poses risks of downtime and update failures, which are unacceptable for production-ready solutions.
• Source code transparency: Opting for transparent AI solutions mitigates the risks of becoming locked into a single vendor due to opaque, “black box” solutions. Understanding what you’re committing to is essential for a sustainable, long-term AI strategy.

AI’s Role and Limitations

• AI complements human decision-making: AI is designed to automate the 80% of tasks that are repetitive, allowing humans to concentrate on the critical 20% involving complex decision-making and creativity that AI cannot handle at this stage.
• Validate AI-generated answers: In the pharma industry, where validated document knowledge is as vast as it is crucial, it’s essential to leverage these existing silos. By forcing AI to utilize these documents for information retrieval, you ensure the AI’s information is also valid and aligns with your work culture.
• Benchmark the retrieval: The RAG system is highly adaptable, and through customization and ongoing enhancements, it can achieve operational accuracy. Regular benchmarking and engaging in a feedback loop are vital for enhancing precision and tracking measurable outcomes. Ability to tune locally deployed AI allows it to further align with corporate ethos.

Benchmarking Your AI

• Evaluate AI performance regularly: It’s crucial to assess AI outputs against established benchmarks to ensure quality. As the feedback loop modifies the model, continuous measurement of these changes is essential for maintaining a positive trajectory. This is also critical for validation efforts.
• Align AI models with goals and KPIs: Continuous user feedback should inform model adjustments, ensuring it meets user needs effectively. Defining and monitoring pre-defined KPIs is vital for tracking the model’s performance and alignment with objectives.

Planning for Scalability

• Begin with specialized AI models: Large, general-purpose models such as ChatGPT, which encompass a wide range of knowledge from presidential trivia to deciphering Egyptian hieroglyphs, are not necessary for our purposes. Our focus is on processing specific internal knowledge and presenting it effectively. Smaller, finely tuned models are more suitable for our needs, offering potentially superior outputs for our specialized queries. This focus is essential for scalability, enabling the AI to handle thousands of queries efficiently without downtime and at reduced processing costs.
• Adopt containerized deployments: Use containerization to streamline deployment and facilitate easy scaling. Leveraging environments like Kubernetes allows for quick adjustments to capacity, avoiding bottlenecks and ensuring smooth operation as user demand changes.

Adoption to existing Workflow

• Tailor AI for specific needs: Achieving production-grade quality necessitates customizing AI to align with the unique needs and workflows of each environment. Success hinges on adapting AI solutions to fit the distinct work cultures and requirements.
• Choose appropriate technology: With the vast array of AI models and infrastructure options available today, identifying the most suitable technologies for your specific needs is crucial for effective AI integration.
• Integrate with current systems: The digital landscape is filled with isolated solutions. Adding yet another can be daunting for companies. The focus should be on integrating AI with existing databases and architectures to streamline processes, rather than starting from scratch.

Stay Flexi

• AI as a launching pad: Integrating AI into your knowledge management system is just the beginning. This integration can pave the way for new functionalities such as code generation, document verification, and regulatory assistance. In practice, a well-implemented AI solution often reveals opportunities that were not initially apparent, with customers frequently identifying these themselves.
• Keep pace with AI advancements: The AI sector is rapidly evolving, with continuous innovations in models and technologies. It’s crucial for your team or chosen provider to remain informed and up-to-date. Embracing controlled updates with the latest advancements ensures your AI solution remains at the forefront, evolving and growing alongside your organization.

Validating AI for production

• Keep validation in mind: In validating AI for production within pharmaceutical environments, a comprehensive risk assessment tailored to the AI tool’s uncertainty, criticality, and process complexity is essential to ensure reliability and regulatory compliance. Use predefined frameworks for validation like AI Maturity Model for GxP [1], Machine learning Risk and Control Framework [2].
• Continuous life cycle audits: Good validation hinges on a diverse benchmark that considers all aspects of AI in business and tests for safety, relevance of the retrieval and quality of AI generated responses. Running on premise provides opportunities for real time analytics that can be leveraged to measure adoption success and compliance metrics.

 

In the landscape of AI integration within the pharmaceutical industry, our journey alongside Arc53 stands as a beacon of innovation, security, and scalability. Through the strategic deployment of the Retriever-Augmented Generation (RAG) system, we’ve not only safeguarded data privacy but also enhanced operational efficiencies, demonstrating the tangible benefits of in-house AI solutions.

Our collaboration highlights a practical yet visionary approach to AI, emphasizing the necessity of aligning technological solutions with core business values and objectives. The insights and achievements we’ve shared underscore the untapped potential of AI to revolutionize industry standards, improve decision-making, and secure sensitive data, all while fostering business growth.

As we look forward to the future, we remain committed to exploring the vast possibilities AI offers to the pharmaceutical sector. Our partnership has paved the way for significant advancements, and we are excited about the opportunity to further innovate and expand our understanding of AI’s role in enhancing industry practices.

Join Us on This Exciting Journey:

Drawing from our extensive experience, including our impactful project with the UK Government, we extend an invitation to those venturing into or currently navigating the realm of AI within pharmaceutical settings. This field is burgeoning with opportunity, and there are still many questions to be explored. Through collaboration and shared insights, we believe significant strides can be made.

If you’re as passionate about harnessing the power of AI to transform the pharmaceutical industry, we’d love to hear from you. Reach out to us at . Whether you’re seeking guidance, looking to collaborate, or simply have questions about integrating AI into your operations, our team is eager to connect and explore how we can achieve groundbreaking results together.

Let’s unlock the full potential of AI in pharmaceuticals, together. Contact us today and take the first step towards a future where innovation and integrity go hand in hand.

Try a Demo: Experience our AI solutions firsthand.

DocsGPT by Arc53 – Check out their work, it’s cool and a big step towards democratizing AI access. They also have a live Demo to check out.

https://docsgpt.arc53.com/

Regulatory Knowledge Assistant by Nd & Aproco – An example of RAG enhanced deployments that leverage regulatory documents. Ask away!

https://nodeviation.com/ai-assistant/

About the writer:

With 6 years in pharmaceutical automation developing projects around the globe, he understands the need for multilingual and fast access to stored knowledge. In his role at Nd in developing adoption methods for AI in pharma he has contributed to developing tools for quick access to regulatory documents to assist validation efforts. Holds a Masters degree in Automation and Robotics from the University of Technology in Poland

Emerging Leader in Open Source RAG AI
Alex is dedicated to making AI open and accessible worldwide. Partnering with No deviation, he’s played a significant role in adapting AI to fit the strict requirements of the pharma industry. He’s also contributed to creating secure AI solutions for the UK Government.

References:
https://ispe.org/pharmaceutical-engineering/march-april-2022/ai-maturity-model-gxp-application-foundation-ai – AI Maturity Model for GxP Application: A Foundation for AI Validation

https://ispe.org/pharmaceutical-engineering/january-february-2024/machine-learning-risk-and-control-framework – Machine Learning Risk and Control Framework