AI powered Summarization for Research Publications

A leading medical research institution with a vast collection of medical literature, ranked amongst top 100 research institutes in United States by QS world rankings.


Problem Statement

The institution faced challenges in efficiently searching for relevant medical literature, with researchers spending 30% of their time on manual searches and filtering through large volumes of data. This process was time-consuming and often led to missed opportunities for breakthroughs.

Our Solution

We developed an AI-powered Enhanced Medical Search solution using a hybrid approach that combines Gen AI and Natural Language Processing (NLP). This solution consists of:

  • Semantic Search for documents: Analyzes medical literature from various sources, including PubMed, Scopus, and Web of Science.
  • Natural Language Processing (NLP) for Text Analysis: Analyzes the retrieved literature using NLP techniques.
  • Generative AI for Summary Generation: Generates concise and informative summaries highlighting key findings.
  • Personalized Recommendations: Learns from researchers’ interactions and feedback to provide personalized recommendations.

Business Benefits

  • Reduced Search Time: Researchers spent 30% less time searching for relevant articles, allowing them to focus on high-value tasks.
  • Improved Search Accuracy: The solution identified relevant articles 25% more accurately compared to traditional methods.
  • Increased Research Productivity: The solution led to a 20% increase in research productivity, with researchers able to publish more papers and make breakthrough discoveries.
  • Cost Savings: The solution resulted in cost savings of $300,000 per year. The Cost Savings are also due to reduced search time, improved research productivity, and enhanced collaboration.
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