Case Study:
Using Large Language Models (LLMs) like ChatGPT to Speed Up Medical Literature Monitoring and Insights
How Large Language Models are helping medical affairs teams increase productivity and reduce medical literature monitoring time
The Client
The client was spending significant time keeping up to date with monitoring and evaluating new medical content. This was a very resource-intense activity and they wanted a way to automate part of this.
The Solution
Using data-driven Natural Language Intelligence (including LLMs) to combine scale and flexibility with contextual understanding, and precision to provide content intelligence for deeper understanding and efficiency.
The Outcome
- Significant Increase in productivity (average from client estimate following implementation is 1000%)
- Significant decrease in literature monitoring time (average from client estimate following implementation is 82-92%)
- Vastly improved tracking to support portfolio products
To achieve these kinds of results, contact Eularis today.
Latest News
Read our latest blogs here.
AI Is Taking the Junior Roles. Who Will Become Tomorrow’s Pharma Experts? Why building judgement matters more than
Medical directors, heads of regulatory, safety leads and HR partners ask me one question more than almost any other. It usually comes after we’ve discussed
How to Overcome AI Resistance and Build an AI-Ready Workforce
In 2026, 81% of physicians reported using AI for work, more than double the 38% the American Medical Association recorded in 2023. Yet AI adoption
Nobody Has Banned AI in HTA Submissions. Everybody Has Made You Answerable for It.
What NICE, CDA-AMC and the new EU JCA principles now expect the moment AI touches your evidence, and the questions nobody has answered yet On