Conversational Search for Clinical Trials
Utilize Amazon Kendra and Large Language models to search & generate content
Overview
Conversational Search for Clinical Trial is powered by Amazon Kendra and Generative AI. It enables Clinical Trial Administrators, Clinical Trial Volunteers, Patients and Researchers in finding information about treatment options and potential clinical trials for a specific condition, they would greatly benefit from clear and concise details about ongoing research conducted by pharmaceutical companies. Through conversational AI, individuals can interact with a bot that provides relevant information about the research and ongoing clinical trials that match their general profile, including treatment descriptions, eligibility criteria, trial locations, and study durations. The bot can address common questions and concerns, ensuring a thorough understanding of the trial process. Additionally, it can assist in drafting a summary/email of inclusion and exclusion criteria for interested individuals
Conversational Search across multiple data sources
This demo uses data from ClinicalTrials.gov. Kendra can ingest million of documents using its pre-built connectors and allow customers to build fast and accurate conversational experience quickly.Amazon Kendra has pre-built connectors for popular data sources such as Amazon Simple Storage Service (Amazon S3), SharePoint, Confluence, and websites, and supports common document formats such as HTML, Word, PowerPoint, PDF, and pure text files.
Get reliable answers with source attribution
This chatbot provides answers along with source links. It uses the RAG(Retrieval Augmented Generation) approach to retrieve information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.
Leverage conversational memory
This chatbot has the ability to remember few of the previous conversation during a chat session. Users do not have to provide all the context in a question
Generate summaries and emails on enterprise data
Users will be able to generate summaries and emails based on the information present in enterprise data sources
Build and deploy similar bots using steps in the blog
Sample questions
- What is acute cholecystitis?
- what are the criteria for participating in the trial for it ?
Architecture
The Workflow includes the following steps:
- 1000s of clinical trails are injested into Amazon Kendra index using the S3 data source connector.
- The LLM is hosted on a SageMaker endpoint.
- An Amazon Lex chatbot is used to interact with the user via the Amazon Lex web UI.
- The solution uses an AWS Lambda function with LangChain to orchestrate between Amazon Kendra, Amazon Lex, and the LLM.
- When users ask the Amazon Lex chatbot for answers from a clinical trails, Amazon Lex calls the Lambda orchestrator to fulfill the request.
- Based on the query, the Lambda orchestrator pulls the relevant records and paragraphs from Amazon Kendra.
- The Lambda orchestrator provides these relevant records to the LLM along with the query and relevant prompt to carry out the required activity.
- The LLM processes the request from the Lambda orchestrator and returns the result.
- The Lambda orchestrator gets the result from the LLM and sends it to the end-user through the Amazon Lex chatbot.
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