This paper details the successful development and deployment of a cutting-edge chatbot, powered by large language models (LLMs) such as Llama 2, tailored for the pharmaceutical and clinical research sector. Our primary innovation lies in streamlining the interpretation of the Study Data Tabulation Model (SDTM) Implementation Guide through a user-friendly, conversational interface.
Central to our development strategy was the utilization of Streamlit, an open-source app framework, for crafting an intuitive and responsive user interface. Streamlit’s flexibility allowed us to create a seamless experience for users, enabling the upload and simultaneous querying of PDF document. This approach drastically simplifies the process of building specification documents for SDTM endpoints. Underlying the chatbot’s intelligence is FAISS (Facebook AI Similarity Search) database. FAISS was instrumental in efficiently managing and querying the large vector embeddings generated by the LLMs.
This integration not only enhanced the chatbot’s performance in document processing but also its accuracy in delivering contextually relevant responses. Further augmenting our tool, we incorporated additional open-source technologies to refine the chatbot’s capabilities. These includes libraries for natural language processing, which facilitated the extraction and interpretation of complex regulatory text, and data visualization tools that enriched the user experience with interactive and informative displays.
Our implementation stands as a testament to the synergy between advanced LLMs and strategic use of open-source technologies. The chatbot, with its robust, multi-faceted functionality, exemplifies a significant advance in digital assistance for clinical research documentation and compliance. It showcases the potential of such technologies to revolutionize the handling of complex documentation in various industries. Thus, the Integration of AI chatbots in clinical Research brings forth legal and ethical considerations where the AI chatbots and clinical professionals work together that require regulatory measures to be considered to optimize the drug development. The main aim is to use AI to enhance the existing process by reducing and optimizing the cycle times.
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