4 minute read
DI Europe Summer 23
Guido Gebhardt
Expediting Workflow with Large Language Models
One of the major issues in radiology is the integration of structured reporting into the radiological workflow, a task which is seen as time-consuming and cumbersome by many radiologists. We interviewed Markus Vogel, MD, Chief Medical Officer at Nuance Communications, about new developments in speech recognition.
◾ Dr. Vogel, many radiologists associate structured reporting with many clicks in another IT system. In addition to RIS and PACS, they now also have to click through the user interface of the reporting solution. To what extent do modern speech recognition technologies help to make the workflow smoother?
When we talk about automated speech recognition, we should start with the microphones. While many radiologists still envision a doctor with a microphone in his hand with digital dictation, modern microphones today are not only much more sensitive, but have also become wireless and integrated. This means that the doctor has both hands free to switch between different applications with the mouse.
As far as speech recognition is concerned, Natural Language Processing (NLP) is still state of the art, although with Dragon Medical One we have also perfected the voice control of the computer hardware. Switching between RIS or PACS and a structured findings solution or jumping to specific input fields is now standard. This eliminates the need to reach for the mouse.
◾ And what about speech recognition and AI? Does Chat GPT have an influence on the reporting of the future and can it further improve the workflow?
Yes of course, but in a different way as many think. ChatGPT is often confused
with a search engine in the media as well but ChatGPT is not there to search for hits like Google. First of all, I would delete Chat in the context of speech recognition and that brings us to the actual topic, since GPT stands for Generative Pretrained Transformer. This means that the system is able to access pretrained content and transform it to generate new texts. And in our opinion, this technology will also be the basis for reporting in the future.
◾ Can you explain this in a little more detail?
The new buzzword is LLM, short for Large Language Model, which is nothing more than an association space that has been trained with radiological findings. And it is the same with Chat GPT: this is not a knowledge database, but a system that has been trained to deal with language on the basis of selected data material.
Let me illustrate this with an example: A Large Language Model is basically a file in which mappings between linguistic units are made. If you ask the system: What is the most common rare disease? You will get the answer: Acute lymphoplastic leukemia. But not because the system knows that, but because the association space is so large and has been trained so well and extensively.
◾ Got it. But how do we now get the sheet to the structured findings solution, for which there are certain specifications – for example from different international professional societies, as to what the sheets should look like and what information they should feature?
The great thing about a large language model is that I can train the association space not only with linguistic content, but also with findings templates. In the future, it will be completely sufficient to freely dictate the findings and the system will independently create a structured findings sheet that is not only formulated in a standardized way, but whose structure is also based on the templates of the various professional societies.
And this is where it gets interesting, of course, to return to your topic: a radiologist would like to have a structured report, but he or she does not feel like putting in the effort that has gone into a structured report so far. Now this is where the Large Language Model comes in.
This means that I simply tell the Large Language Model: Look, I have input A here, this is what a structured report should look like in the end. Input B, a freely spoken text, is added to this. The task for the LLM is to convert the radiologist’s text into the specified form. The structured report is ready.
◾ And if we now want to integrate the measured values of the AI solution?
Then we simply tell the language model: Now you also have input C. These are the measured values of the AI solution, which you add to the findings at the appropriate points. This is exactly what Large Language Models can do perfectly. That is what they are for, and that is what is going to happen in medicine in the next few months.
