Your NextGen AI ecosystem
Use Cases and Deployment Scope
We use this to tune the llm, do prompting, deploy the models and then iterate for our multiple projects in medical domain like claims system, aba, chatbots etc. We are solving medical problems like medical record summarization, chatbot for QA from medical Record , ic code lookup and claims submission forms. It gives a good ecosystem of tools for miltiple usecases and all embedded within the same environment thats a great advantage and also using langchain is also great.
Pros
- Integration with other systems
- Deployment option within same exosyatem is great and can easily deploy any model .
- Security layer for governing and hace insights to see the predictions and ai studio is also good
Cons
- IBM watsonx.ai is expensive than other platforms.
- Limited integraions though it has many but still some tools integrations not there for medical usecase
- Its little difficult to learn as right now not many open reseouces
- Community is not that strong to get any answer
Return on Investment
- For one of the usecase we were able to save 1.3 million dollar by creating the COB project in 2 months by leveraging the whole ecosystem and savibg of approximately 2800 hrs per annum
- For the icd usecase wherre we have used the graph memgraph using docker to deploy it and created graphs and then created inference pipeline saved 20 minutes per medical coder and approximately of 3.5 million dollar savings as claims submission verifying on the payer side is a lengthy process
Other Software Used
IBM Watson Natural Language Understanding, IBM Watson Speech to Text, IBM Watson Visual Recognition (discontinued)
