This blog post discusses the use of large language models (LLMs) in building intelligent biomedical chatbots. LLMs, such as GPT-3.5-turbo, Bio-GPT, and Falcon, have the ability to recognize complex linguistic patterns and generate human-like text. In the biomedical space, these models can be utilized to create chatbots for question-answering and interacting with knowledge graphs. By combining state-of-the-art language processing algorithms with medical understanding, these chatbots can engage in intelligent conversations and provide personalized support. The blog post provides instructions on creating a chatbot interface using Streamlit and demonstrates how to use GPT-3.5-turbo, Bio-GPT, and Falcon for different biomedical tasks. The limitations of LLMs are also discussed, emphasizing that their answers should not replace expert opinion. Overall, this blog post serves as a starting point for developing sophisticated biomedical chatbot applications.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Use of large language models in biomedical chatbots | Integration of language models in chatbot development | More advanced and intelligent biomedical chatbots | Advancement in natural language processing and medical understanding |
| Combining multiple language models for specific use cases | Integration of multiple models in one chatbot | Enhanced functionality and accuracy in chatbot responses | Improvements in model integration and finetuning techniques |
| Development of a chatbot interface using Streamlit | Creation of user-friendly interface for chatbot interaction | Improved user experience and accessibility | Focus on user-centered design and interface development |
| Biomedical KG question answering using GPT-3.5-turbo | Use of GPT-3.5-turbo for biomedical question answering | More efficient and accurate biomedical knowledge graph querying | Need for accessible and user-friendly querying in biomedical research |
| Biomedical text generation using Bio-GPT | Utilization of Bio-GPT for biomedical text generation | Enhanced generation of biomedical text and insights | Expansion of text generation capabilities in biomedical research |
| General question answering using Falcon | Application of Falcon for general question answering | Improved performance and accuracy in general question answering | Advancements in autoregressive decoder-only models like Falcon |
| Deployment of chatbot over Streamlit cloud | Hosting the chatbot on Streamlit Cloud platform | Increased accessibility and availability of the chatbot | Growing demand for easy-to-deploy and scalable chatbot solutions |