Quick Win GenAI Solutions for Pharma: Streamlining Regulatory, Clinical, and Safety Processes

The possibilities for building custom GenAI solutions to streamline internal processes in the pharmaceutical industry are endless. However, determining where to begin can be challenging. This guide explores the benefits and speed of GenAI development, provides tips for identifying quick-win solutions, and suggests starting points in the areas of regulatory affairs, clinical trials, and safety.

GenAI Solution Timelines:

While the vast amount of data AI solutions can handle is widely discussed, the rapid development speed is often overlooked. The Software Development Kits (SDKs) available for GenAI solution development have significantly reduced development timeframes, even for complex solutions, making them achievable within weeks instead of months.

For example, a typical chatbot that might have taken three months to develop five years ago can now be built in a week or less.

How to Identify Quick-Win Solutions:

Embrace the “Fail Fast” approach. Given the rapid development speed of GenAI solutions, exploring a wider range of use cases for experimentation is preferable to searching for the “perfect” one.

Involve developers from the start. When identifying use cases, collaborate with a development team or partner (like us!). As your business team identifies potential applications, the developer can support by creating proofs of concept (POCs) that can be validated by your teams for functionality and usefulness. This approach, “Think. Build. Get Feedback. Repeat,” applies to both product ideation and GenAI development.

What to keep in mind

• QuickWin solutions can be built using pre-trained LLMs (direct integration with LLM or through RAG architecture). This restricts the context of the output.
• To get more contextual output one would have to either Fine tune models or build foundational models. This approach is both cost and time intensive.
• More about them in our subsequent blogs
• Also it’s crucial to maintain responsible AI practices during development and testing to mitigate potential biases and ensure ethical outcomes.

Where to Start:

Here are some areas where you can easily get started with GenAI solutions:

• AI Assistants: Many productive hours are spent navigating document libraries like SOPs, training documents, and internal policy documents. Consider integrating these with a Large Language Model (LLM) to provide employees with the information they need, when they need it. This not only saves time but also promotes compliance.
• Text Summarization: The pharmaceutical industry is information-intensive, with multiple drafts of text being written and rewritten for emails, meeting transcripts, conferences, and submission documents. Building an internal text summarization tool can address security concerns regarding platforms like ChatGPT and Co-pilot, while empowering employees to efficiently structure and save precious time.
• Archival Search: Finding a specific decision made during a product management meeting five years ago can be time-consuming. Enhance your archival document search with AI to enable text-based searches instead of relying solely on document titles, saving valuable time.

Conclusion:

When choosing GenAI use cases, prioritize both speed and scalability. Experimentation is key, as this evolving technology will offer increasingly diverse solutions over time. Avoid analysis paralysis and embrace the future of GenAI – don’t miss out on this race!

Ready to explore how GenAI can streamline your Pharma processes? Contact us today at info@slickbit.com