Problem

The life sciences industry is burdened by long research and development (R&D) cycles and labor-intensive processes in clinical trials, manufacturing, and compliance. There is a need to accelerate drug development, automate time-consuming tasks like document creation, and improve efficiency while maintaining compliance with regulations. The industry is also facing challenges in handling the explosion of digital data generated in various areas, such as commercial, supply chain, clinical, and pharmacovigilance, which come in different formats, including unstructured text, images, PDFs, and emails.

Solution 1 : Leverage AI

Generative AI is a type of artificial intelligence that can create new content, such as text, images, or code, based on patterns it has learned from existing data. By using generative AI in various areas of the life sciences industry, several processes can be automated, and human teams can be assisted, leading to faster and more efficient drug development and improved productivity.

Use cases

  1. AI for Medical Legal Review (MLR): Generative AI can process digital marketing content at scale and produce effective MLR outputs, simplifying and accelerating the complex and time-consuming process.
  2. Generating Clinical Study Reports (CSR): Generative AI can create a “first attempt” report, reducing human effort by 80% and speeding up the process while ensuring consistency.
  3. Adverse Event (AE) Narrative Generation: Leveraging generative AI can significantly reduce costs and accelerate time-to-market related to AE narrative generation by 30%-50%. It improves scalability, quality, and consistency of generated reports, which is particularly critical for highly regulated tasks.
  4. Accelerate mRNA Medicines Design: Generative AI can be used to design mRNA medicines with optimal safety and performance, enabling faster and more efficient drug development.