Grounding Medical Q&A: Enhancing Model Performance with ChatGPT Plugins and Knowledge Graphs

In the realm of healthcare, ensuring accurate and reliable medical question and answer (Q&A) systems is of paramount importance. This article explores the concept of grounding medical Q&A by leveraging the capabilities of ChatGPT plugins and knowledge graphs. By combining these technologies, we can improve model performance, reduce inaccuracies, and provide trustworthy AI systems. In this article, we delve into the significance of grounding strategies, the role of knowledge graphs, and the benefits of using ChatGPT plugins in the medical domain.

THE POWER OF CHATGPT IN MEDICAL Q&A

Medical Q&A datasets serve as benchmarks for evaluating the effectiveness of models in answering medical questions. Recently, models like Palm 2 and ChatGPT 4 have shown promising results in addressing the challenges of medical Q&A. However, these large language models may suffer from hallucinations, where they confidently provide inaccurate responses. To overcome this limitation, grounding strategies come into play.

Grounding Strategies

Hallucinations in large language models often occur due to their limited understanding and reasoning capabilities. Grounding strategies involve employing additional information and API calls to minimize hallucinations and enhance model performance. However, large language models lack real-time knowledge updates and database functionality. This is where knowledge graphs prove invaluable.

Knowledge Graphs

Knowledge graphs store explicit information about entities, relationships, and facts, allowing for accurate and structured knowledge retrieval. While large language models excel in general knowledge and language processing, knowledge graphs specialize in structured knowledge and accuracy. By unifying the strengths of large language models and knowledge graphs, we can create more reliable and trustworthy AI systems.

ChatGPT Plugins

ChatGPT plugins play a crucial role in grounding medical Q&A. These plugins enable real-time interaction, verification, source attribution, and context windows, thereby maintaining the conversation's context. Different grounding strategies, including pre-training, fine-tuning, and prompt engineering, can enhance model performance and mitigate inaccuracies. Pre-training and fine-tuning make models more domain-specific, while prompt engineering enriches the context for better responses.

Models vs. Code

Differentiating between models and code is essential in utilizing them effectively. Models are statistical, scalable, and probabilistic, allowing for generalization. On the other hand, code is deterministic and computable, ideal for precise calculations and deterministic results. By understanding their strengths and use cases, we can harness the full potential of models and code in medical Q&A systems.

Realizing the Potential: Case Studies and Demonstrations

To showcase the power of grounding medical Q&A with ChatGPT plugins and knowledge graphs, we utilized GenomOncology’s Match API. You can watch a short demo where we showcase how to integrate ChatGPT with plugins that are designed for precision oncology in order to provide users access to the latest clinical trial data, information on the most effective treatments for a particular diagnosis, and much more, here. The demo starts at minute 18.

Looking Towards the Future

As technology progresses, the integration of large language models, knowledge graphs, and medical Q&A systems will continue to evolve. Ongoing research focuses on improving model controllability, developing open-source models, and running large language models behind firewalls. Collaboration between researchers, developers, and medical institutes holds the key to advancing this field further.