Summary
This article explores how to build a generative AI medical device training assistant using NVIDIA NIM microservices. It highlights the challenges and potential applications of generative AI in medical devices, focusing on creating a retrieval-augmented generation (RAG) pipeline with optional speech capabilities to answer questions about medical devices using their instructional for use (IFU) documents.
Building a Generative AI Medical Device Training Assistant
Medical devices are becoming increasingly sophisticated, with a record number of new and updated devices being authorized by the FDA every year. This rapid innovation presents a challenge: clinicians and patients need comprehensive training to use these devices properly and safely. Moreover, troubleshooting issues with these devices can be complex. To address this, a generative AI medical device training assistant can provide accurate, hands-free answers in real-time.
What is Retrieval-Augmented Generation (RAG)?
RAG uses deep learning models, including large language models (LLMs), for efficient search and retrieval of information using natural language. This technology allows users to receive easy-to-understand instructions for specific questions in a large text corpus, such as an IFU document. By integrating RAG with speech AI models like automatic speech recognition (ASR) and text-to-speech (TTS), users can communicate with these advanced generative AI workflows using their voice, which is particularly useful in sterile environments like operating rooms.
NVIDIA NIM Microservices
NVIDIA NIM inference microservices are GPU-optimized and highly performant containers for these models. They provide the lowest total cost of ownership and the best inference optimization for the latest models. By integrating RAG and speech AI with the efficiency and simplicity of deploying NIM microservices, companies developing advanced medical devices can offer clinicians accurate, hands-free answers in real-time.
Building the RAG Pipeline
To build a RAG pipeline with optional speech capabilities for answering medical device questions using its IFU, you can follow these steps:
- Access NIM Microservices: Sign up for free API credits on the API Catalog at build.nvidia.com or deploy on your own compute infrastructure.
- Build and Start Containers: Use Docker compose files to launch containers with NIM microservices and a vector database.
- Ingest the Device Manual: Upload your IFU in the “Knowledge Base” tab.
- Retrieve and Generate Answers: Navigate to the “Converse” tab to begin the conversation with the IFU. Ensure you click “Use Knowledge Base” to use the IFU as a knowledge resource.
Example Use Case
| Step | Action | Outcome |
|---|---|---|
| 1 | Access NIM Microservices | Obtain API credits or deploy on own infrastructure |
| 2 | Build and Start Containers | Launch containers with NIM microservices and vector database |
| 3 | Ingest the Device Manual | Upload IFU in the “Knowledge Base” tab |
| 4 | Retrieve and Generate Answers | Begin conversation with the IFU in the “Converse” tab |
Benefits and Challenges
- Benefits: Provides accurate, hands-free answers in real-time, enhancing training and troubleshooting for medical devices.
- Challenges: Ensuring safety and effectiveness, mitigating uncertainties, and addressing regulatory implications.
Conclusion
Building a generative AI medical device training assistant using NVIDIA NIM microservices offers a promising solution for enhancing training and troubleshooting in the medical device industry. By leveraging RAG and speech AI, companies can provide clinicians with accurate, hands-free answers in real-time, improving the overall safety and effectiveness of medical devices. However, addressing the challenges of regulatory compliance and ensuring the reliability of AI outputs remains crucial for widespread adoption.