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Revolutionizing Document Search and Summarization in Life Sciences with Generative AI

04 Aug 2023
In the fast-paced world of life sciences, medical professionals often find themselves grappling with vast amounts of data stored in large documents. These documents are not easily searchable and consumable, posing significant challenges in extracting valuable insights efficiently from this database. Life Sciences companies continue to deploy traditional models of search, which more often than not, are counterproductive. At the same time, inability to reuse content leads to inefficiencies that are completely avoidable.
4 challenge areas that the industry needs to surmount
Document Search and Summarization Challenges
How can Generative AI help?
Making Large Documents Searchable and Consumable
Difficulty in searching through extensive documents. In an ideal scenario, a user should be able to pull a document from the document management systems (DMS) based on defined criteria and then run a search for specific content within the document. However, it has been a challenge historically.
Generative AI can be leveraged to create tagged and searchable document databases, highlight keywords, enable document summaries, and section-level summaries.
Extracting Insights and Personalized Summaries
Extracting actionable insights from complex medical documents can be a daunting task
Solutions leveraging Generative AI platforms can create comprehensive summaries that go beyond simple keyword extraction. The structure and ruleset defined at the backend allow for the creation of role-based summaries, thematic summaries, and even summaries tailored to specific sections of interest. This not only saves time but also enables professionals to quickly identify the value and relevance of a document, answering the crucial question of "What's in it for me?"
Consolidating Content Search Across Documents
Manually searching inside documents and even consolidating content search across multiple documents is a time-consuming and error-prone process
The querying process can be streamlined for single or multiple documents and integrated with a conversational chatbot for a seamless user experience.
Enabling Downstream Insights and Future Use
Difficulty in seamless cascading of valuable insights from search results to downstream systems for maximum benefit
By integrating with a business process management (BPM) engine, users can leverage the extracted insights to drive informed decision-making. Furthermore, the solution should allow users to save queries for future use, empowering them with a knowledge repository that can be accessed whenever needed
How is Indegene empowering life sciences organizations to transform their document search and summarization processes?
At Indegene, we've developed NEXT Search and Summarization, our proprietary in-house solution powered by Generative AI. Key features include:
Summarizes Complex Documents
Easily create concise summaries from comprehensive documents
Automatic Keyword Generation
Generate relevant keywords for efficient document analysis
Smart Search Functionality
Perform intelligent searches across single or multiple documents
Trust and Explainability
Cite specific references for answers, including sections and pages
NEXT Search and Summarization harnesses the power of Azure OpenAI suite, utilizing large language models (LLMs) to create diverse applications like chatbots, question-answering systems, summarization systems, and code generation systems. To streamline LLM-powered app development and deployment, Langchain, an open-source framework, is deployed that enables easy AI platform integration, data-aware applications, and chaining for intricate solutions.
By making large documents easily searchable and consumable, extracting valuable insights, consolidating content search, and providing downstream insights, the solution revolutionizes document search and summarization. The workflow of this technology involves several crucial steps that enable these advancements:
Workflow
What does this do?
Business Value
1
Document loading
Transforming raw data into standardized document objects, including main content and metadata
Ensures consistent and organized data for further processing
2
Document splitting
Using splitters like Character Text Splitter or Sentence Text Splitter to enhance processing efficiency and application performance
Improves data parsing efficiency
Optimizes resource utilization
3
Data storage
Storing all data in Vector DB powered by Pine Cone
High-performance AI-enabled search enabling precise answers to queries with built-in explainability in the model
Flexible indexing and scalable architecture
Enhanced data retrieval
4
Retrieval techniques
Employing advanced methods like Maximum Marginal Relevance (MMR), self-query, and compression
Diverse information retrieval
Precise filtering based on metadata
Focus on essential details
5
Output
Utilizing language models for search and chat applications, aided by MapReduce, Refine, and Map Rerank algorithms
Improved search results
Refined responses
Enhanced conversational capabilities with chat history integration
With the ability to generate role-based and thematic summaries, facilitate conversational chatbot interactions, and enable future search and analysis, NEXT Search and Summarization empowers professionals to navigate the complex landscape of life sciences efficiently.
Get in touch with us to learn more about how our cutting-edge generative AI solution can revolutionize your document search and summarization processes in the dynamic life sciences industry.

Authors

Vikas Tripathi
Vikas Tripathi
Ritesh Dogra
Ritesh Dogra
Tridisha Goswami
Tridisha Goswami