CASE STUDY: Agentic AI Workflows
Transforming Medical Research with Agentic AI Workflows
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Client Overview
A multinational healthcare organization with the main goal of helping people live healthy lives. The company has more than 115,000 workers and more than 4,000 FLMs in more than 160 countries, finding innovative ways to improve people’s lives. It provides a wide range of industry-leading solutions that complement positive long-term healthcare trends in both developed and emerging economies.
The Challenge
The organization’s literature review process was fragmented, manual, and reliant on external vendors, resulting in high costs, inefficiencies, and poor scalability. Lack of an internal solution limited flexibility in search strategy design and reduced operational agility. Repetitive manual work delayed research and decision-making, while experts spent significant time on low-value tasks instead of analysis.
Project Goal
We focused on three core pillars:
Define question: Converts research queries into structured PICO format.
Build strategy: Generates and refines MeSH terms using tools like PubMed and LangGraph.
Collect data: Searches databases and allows interactive refinement of results.
- Filter results: Applies inclusion/exclusion criteria to shortlist relevant studies.
Intelligent Healthcare Research Platform
Built a scalable, AI-powered platform that streamlines systematic literature reviews with an intuitive interface, enabling faster research, efficient collaboration, and seamless future scalability.
The Results
99.3%
faster literature review turnaround
86%
ncrease in relevant references identified
1.9×
more relevant references identified
5.0
What Our Client Said
"In healthcare, speed and accuracy are essential. By leveraging LLM-powered automation, we significantly accelerated literature reviews and reduced the manual burden on experts. This not only improved efficiency but also enabled faster, more informed decision-making across research and clinical teams."
— Dr. Emily Carter
Chief Medical Officer
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