This blog post discusses the development of a biomedical chatbot using large language models (LLMs) such as OpenAI’s GPT-3.5-turbo, Bio-GPT, and Falcon. It highlights how these models can be utilized for intelligent conversations and personalized support in the biomedical domain. The chatbot interface is created using Streamlit, allowing users to choose between different LLM functionalities: biomedical knowledge graph question answering, biomedical text generation, and general question answering. The article also covers the process of deploying the app on Streamlit cloud and emphasizes the limitations of LLMs, recommending that their outputs not be seen as substitutes for expert opinions. Moreover, it suggests finetuning these models for specific tasks for more reliable results.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Emerging Biomedical Chatbots | Development of chatbots using advanced LLMs for the biomedical sector. | Shift from traditional health information systems to AI-driven chatbots for personalized support. | Widespread adoption of AI chatbots in healthcare for patient support and information dissemination. | Growing demand for accessible healthcare information and support through user-friendly AI interfaces. | 4 |
| Integration of Multiple LLMs | Combining various LLMs like GPT, Bio-GPT, and Falcon for enhanced performance. | Transition from single-model applications to multi-model systems for complex biomedical queries. | Sophisticated AI systems that leverage multiple models for diverse biomedical applications. | Need for improved accuracy and understanding in biomedical AI solutions. | 4 |
| Knowledge Graphs in Biomedicine | Use of knowledge graphs for querying biomedical information. | Shift from unstructured data to structured knowledge representation in biomedicine. | More efficient data retrieval and knowledge discovery in biomedical research and practice. | Advancements in graph database technologies and AI integration for data management. | 5 |
| User-Friendly Interfaces for Complex Queries | Streamlit-based interface for user interaction with biomedical data. | Move from technical query languages to natural language interfaces for end-users. | Increased accessibility of complex biomedical data for non-expert users through intuitive interfaces. | Desire to democratize access to healthcare information and research. | 4 |
| Personalized Healthcare Solutions | Chatbots providing personalized medical information and support. | From generic medical advice to tailored assistance based on user queries. | Personalized healthcare interactions powered by AI, improving patient engagement and satisfaction. | Emerging trends in personalized medicine and patient-centered care. | 5 |
| Limitations of Current LLMs | Acknowledgment of inherent limitations in current LLM applications. | Shift from over-reliance on AI for medical advice to more balanced approaches. | Recognition of the need for human oversight in AI-driven medical consultations. | Growing awareness of the ethical implications and risks of AI in healthcare. | 4 |
| name | description | relevancy |
|---|---|---|
| Dependence on Large Language Models | Relying on LLMs for biomedical advice poses risks if they provide incorrect or misleading information. | 5 |
| Data Privacy and Security | Storing sensitive biomedical data and API keys on public platforms may lead to unauthorized access and data breaches. | 4 |
| Potential for Hallucinations | LLMs may generate false information (‘hallucinations’), which can mislead users seeking accurate medical guidance. | 5 |
| Inaccessibility of Knowledge Graphs | Users may struggle to interact with biomedical knowledge graphs due to the complexity of required queries. | 3 |
| Model Bias and Misrepresentation | LLMs may inherit biases from training data, potentially leading to misrepresentation of medical conditions. | 4 |
| Need for Expert Oversight | Automated systems should not replace human experts; oversight is crucial to ensure proper guidance is given. | 5 |
| Integration and Compatibility Issues | Difficulty in integrating various models and platforms may hinder user experience and functionality. | 3 |
| Ethical Concerns in Biomedical Applications | Utilizing AI in healthcare raises ethical questions regarding accountability and informed consent. | 4 |
| name | description | relevancy |
|---|---|---|
| Biomedical Chatbot Development | Creating chatbots that utilize large language models for biomedical applications, enabling intelligent conversations and personalized support. | 5 |
| Integration of Multiple LLMs | Combining various large language models under a single platform to enhance functionality and user choice in biomedical tasks. | 4 |
| Natural Language Queries for Knowledge Graphs | Using natural language processing to convert plain English queries into specialized queries for knowledge graphs, making complex data accessible. | 5 |
| Biomedical Text Generation | Leveraging advanced LLMs like Bio-GPT for generating biomedical text, enhancing research and communication in the field. | 4 |
| Streamlit for Application Deployment | Utilizing Streamlit to develop and deploy interactive web applications for biomedical chatbots, facilitating user engagement. | 4 |
| User-Centric Interface Design | Designing chatbot interfaces that enhance user experience by allowing easy input and interaction with biomedical information. | 4 |
| Continuous Improvement and Development | Iteratively developing and refining chatbot functionalities based on user feedback and technological advancements. | 3 |
| Expert Opinion Integration | Highlighting the importance of expert validation in responses generated by LLMs, ensuring reliability in biomedical contexts. | 5 |
| description | relevancy | src |
|---|---|---|
| Advanced models like GPT-3.5-turbo, Bio-GPT, and Falcon that process natural language and generate human-like text. | 5 | f96c3e0990df3993b30f57a358c76d6a |
| Chatbots powered by LLMs that assist in biomedical question-answering and knowledge graph interactions. | 5 | f96c3e0990df3993b30f57a358c76d6a |
| Utilizing LLMs to convert plain English queries into Cypher for querying biomedical KGs. | 4 | f96c3e0990df3993b30f57a358c76d6a |
| A transformer-based model specifically designed for biomedical text generation and mining. | 5 | f96c3e0990df3993b30f57a358c76d6a |
| A prominent graph database technology for managing complex relationships in biomedical data. | 4 | f96c3e0990df3993b30f57a358c76d6a |
| A platform for deploying web applications easily, suitable for hosting small-scale apps. | 3 | f96c3e0990df3993b30f57a358c76d6a |
| Using transformer models like BioGpt and Falcon for generating biomedical text responses. | 4 | f96c3e0990df3993b30f57a358c76d6a |
| name | description | relevancy |
|---|---|---|
| Biomedicine and AI Integration | The use of large language models in creating intelligent biomedical chatbots for personalized medical support. | 5 |
| Knowledge Graph Accessibility | Challenges in accessing and querying biomedical knowledge graphs due to complex programming requirements. | 4 |
| Limitations of LLMs | The evolving limitations of large language models in providing accurate medical advice should be acknowledged. | 5 |
| Trust in AI-generated Medical Information | Concerns regarding the reliability of medical information generated by AI chatbots. | 4 |
| Finetuning AI Models for Specific Tasks | The need for finetuning models for specific biomedical applications to improve accuracy and reliability. | 5 |
| Deployment of AI in Healthcare | The challenges and considerations in deploying AI applications in healthcare environments. | 4 |
| Data Privacy and Security | Concerns related to the privacy and security of sensitive biomedical data in AI applications. | 5 |
| Future of AI in Biomedical Research | Potential advancements in AI and their implications for biomedical research and patient care. | 5 |