Recently, TrialAssure attended the 5th Annual Pharma AI Summit in London. This event brought together industry leaders and innovators to discuss the transformative potential of AI in the pharmaceutical sector and strategies that they are adopting to jumpstart or accelerate their generative AI journey.

During the event, AstraZeneca’s Head of R&D Architecture Hassan Abba highlighted research from McKinsey, stating that generative AI could create USD 60-110 billion annually in economic value for pharmaceutical and medical product industries. From this, it is clear that AI is no longer a buzzword but a real driver of change in our industry.

One-size-fits-all does not work here

There is no “one-size-fits-all” approach to GenAI adoption in life sciences, as systems, processes, and technical maturity can vary across organizations. A systematic approach is essential, and could look like the following:

  • Strategize: Develop a comprehensive GenAI strategy aligned with business users.
  • Define: Identify potential use cases across business functions.
  • Evaluate and Prioritize: Focus on high-value, low-investment use cases by assessing market desirability, business viability, and technical, scientific, and clinical feasibility.
  • Pilot: Create proof of concepts for prioritized use cases to validate feasibility and value.
  • Scale: Expand successful pilots to full operational adoption.

High-value use case where GenAI excels

Generative AI can greatly improve efficiency across the clinical development lifecycle, delivering significant economic benefits in key areas. We have seen this occur by:

  • Accelerating drug discovery through faster compound identification, optimized drug design, and efficacy prediction.
  • Reducing costs and speeding up clinical trial processes.
  • Streamlining clinical trial documentation, transparency requirements, and regulatory submissions.
  • Boosting net present value (NPV) by enhancing global health authority interactions, quality control, and signal management.

Nataraj Dasgupta, Vice President of Advanced Analytics at RxDataScience (a Syneos Health company), spoke during the Pharma AI Summit, noting that large language models (LLMs) are most effective for tasks like medical and regulatory content authoring.

Development struggles are real

Building GenAI solutions in-house poses several risks, like high failure rates, lack of expertise, long development cycles, compliance challenges, and cost overruns, to name a few.

Several thought leaders in London recommended leveraging off-the-shelf solutions with customizations or co-developing with trusted partners to lower costs and speed up time-to-value.

Martin Keywood, Sr. Director of Scientific Data Technology at Parexel, shared that while many Generative AI companies generate buzz, their actual execution capabilities are often limited.

The ideal partner should combine domain expertise with a track record in clinical development, helping organizations quickly pilot and scale solutions. From our experience at TrialAssure, partnerships are crucial for GenAI success in organizations that are looking for strong ROI.

More on partnerships with TrialAssure here.

Leveraging TrialAssure’s expertise in GenAI

For organizations looking to accelerate their GenAI journey while minimizing risks, exploring partnerships with established platforms like TrialAssure’s LINK AI could be a strategic next step.

With a tenure of experience, TrialAssure is a trusted partner to leading biopharma, biotech, and clinical research organizations. Our comprehensive approach to Generative AI adoption is tailored to our clients’ unique needs and stage in their GenAI journey. Take a look at a few potential approaches:

Approach 1

  • Leverage our domain and AI expertise for strategic AI road mapping.
  • Identify and prioritize high-value use cases.
  • TrialAssure LINK AI platform for quick pilot and scaled adoption of high value GenAI use cases.

Approach 2

  • Leverage TrialAssure LINK AI, a highly configurable GenAI medical writing solution, to accelerate your GenAI adoption with pre-built medical and regulatory writing use cases
  • Customize and expand TrialAssure LINK AI platform to support additional use cases beyond medical writing.

Key Benefits of these approaches:

  • Rapid Development: Cut development time by up to 95 percent.
  • Cost-Effective: Fixed pricing, lower total cost, and faster ROI.
  • Proven Success: Scalable, customizable platforms with industry track records.
  • Immediate Impact: Realize benefits within weeks.

This article was authored by Himanshu Kumar Singh. For questions regarding the content within this article, email info@trialassure.com.

Share