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projectThis project aims to provide clinical trial information in a more intuitive and user-friendly manner by leveraging an LLM-based chatbot.
Patients seeking to participate in clinical trials currently struggle to understand complex medical terminology and vast amounts of information. Existing search systems rely on rigid keyword-based searches, making it difficult for users to quickly find the information they need. This project aims to provide clinical trial information in a more intuitive and user-friendly manner by leveraging an LLM-based chatbot.
This system analyzes patient questions using a large language model and provides personalized clinical trial information. Key features include:
It provides an AI-based question-answering system that analyzes user queries using natural language processing technology to deliver the most appropriate clinical trial information. By linking with specialized medical data, it provides reliable answers and supports explanations tailored to patient comprehension.
To provide clinical trial information, it updates the latest clinical trial data by linking with the National Clinical Trial Information System. It collaborates with major hospitals and research institutions to provide reliable information, helping patients easily find suitable clinical trials.
It also provides personalized accessibility information, such as public transportation and convenience facilities. By analyzing patient health status, age, location, and other information, it recommends the optimal clinical trial. It continuously improves its recommendation algorithm based on user feedback, increasing the likelihood of patient participation.
Once this system is implemented, patients will be able to access complex medical information more easily and find clinical trials suited to their health status more efficiently.
It is also expected to provide researchers with a larger pool of potential participants, thereby accelerating the pace of clinical trial progress.
Enhance clinical trial success rates by removing barriers to participation, providing personalized accessibility information, and ensuring participation accessibility.
Launch year: 2024
Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.1, https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=28997&parentSeq=29032
In partnership with
National Information Society Agency (NIA)
In partnership with
National Information Society Agency (NIA)
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