There is a lot of talk around the town globally that once AI takes over, several jobs will cease to exist. However, as we enter the world of radiology, a natural query arises -will AI replace radiologists?
Radiology is essentially the technique used to diagnose using medical imagery. Let’s head on to the question with a stepwise approach.
- What is the most common misconception out there about AI in radiology?
For those who are unaware in this area, the quick reaction is that it is going to come down to radiologists vs. AI. People often ask when will radiologists replace AI, but that is just the wrong question. It is like someone asking if calculators would replace accountants. It just does not work that way. We are not looking out for a day when radiologists are out of practice and it is all replaced by computers. But radiologists equipped with AI as an aid will be better and more efficient than radiologists without AI.
- We are starting to see more and more AI-related solutions gain FDA approval, including some specifically designed to analyze imaging results. Does this progress mean radiologists are a lot closer to experiencing the benefits of AI firsthand? Movement in AI is still fairly slow. You still need to have access to data, have the ability to curate data, find a specific problem to solve, obtain high-performance computing and validate your data before you go through regulatory approval. And then, once you gain that approval, you still have to market your solution and connect it into a clinical workflow. We are seeing a lot of things gain FDA approval, but that is still far from full implementation. There are also still some broad challenges in the market for vendors working in AI. Things like defining the exact use cases, determining what people will pay for these solutions and work out the full business model.
- How has radiology responded to AI so far?
The response has been broad, but with little harmonization, at least initially. Each supplier looks at this through their own lens—they say, “I have a PACS, so I’m going to make a PACS solution,” or, “I work in visualization, so I’m going to make a visualization tool.” And that is fine because AI works by improving a particular tool or function. But the challenge in medical imaging is handling all of these different perspectives and making it so they can come together. That is one of the reasons the ACR started the DSI, to help bring these perspectives together through common standards and definitions of AI use cases. One example would be if someone created a pulmonary nodule detector and then someone else created another pulmonary nodule detector. There is no reason why those two should not output the same values, the same numbers, for the same examination. So if, say, a patient moves from one location to another location, they might be getting different numbers. The ACR is looking at ways to define these use cases much more consistently so that the creation of AI applications will be consistent. We would still allow room to evolve and innovate, but the inputs and outputs need to be consistent with manufacturers of other solutions to be able to integrate them.
It is about time people look at AI as an aid or tool rather than an enemy. This can be accomplished by creating awareness among the general population.