How GenAI got much better at medical questions - thanks to RAG
Scholars at Stanford built a system, called Almanac, that will retrieve medical information in real-time in response to a Gen AI prompt, and found it improved answers to medical questions written by doctors.
This is the year that many parties using generative artificial intelligence will try to give the programs something resembling knowledge. They will mostly do so using a rapidly expanding effort called "retrieval-augmented generation," or, RAG, whereby large language models (LLMs) seek outside input -- while forming their outputs -- to amplify what the neural network can do on its own.
RAG can make LLMs better at medical knowledge, for example, according to a report by Stanford University and collaborators published this week in the NEJM AI, a new journal published by the prestigious New England Journal of Medicine.
RAG-enhanced versions of GPT-4 and other programs "showed a significant improvement in performance compared with the standard LLMs" when answering novel questions written by board-certified physicians, report lead author Cyril Zakka and colleagues.
