AI's rapid maturation and pace of development is transforming healthcare. While augmenting and enhancing existing clinical workflows, AI also brings subtler, less obvious changes. Those medical specialties that are early adopters can point the way and provide important lessons as more areas of healthcare – and life in general – take the AI plunge.
The FDA has cleared more AI algorithms for radiology than any other field, and all trends point toward an increasing number of clearances this year. Researchers and companies have developed algorithms to detect hemorrhages, tumors, embolisms, and many more pathologies, ranging from the common everyday conditions to rare ones.
While this explosion in AI-driven creativity holds great promise, it is not sustainable in its present form. As the saying goes, it’s possible to have too much of a good thing, and this is especially true when it comes to AI algorithms.
The 2025 Workflow
Imagine it’s 2025, and hundreds of AI algorithms for radiology are available. Every radiologist uses AI tools for all the mundane tasks, like detecting, measuring, comparing and collecting data.
When our 2025 radiologist interprets a head CT that’s been analyzed by 50 different algorithms, what do they see? Perhaps a brain bleed marked with a big red star provided by Company X. The bleed is also measured, with the measurement marked with yellow lines and numbers provided by Company Y. Then, they see bone fractures, circled in blue, provided by vendor Z. Of course, the brain volume is also measured, by company A, with red segmentation lines.
However, it's not just about the presentation layer, with its Jackson Pollock confusion of colors and shapes. There is also a huge problem with false positives. A good algorithm might have approximately a 5-percent false-positive rate, but if 10 algorithms are running together, all of those “5 percents” begin to add up, resulting in an unacceptable 50-percent false-positive rate. Add to this that each algorithm might have different exclusion criteria and, perhaps, even different measurement units.
In short, if many independent algorithms are applied on the same anatomical region, AI could create an intolerable user experience.
This future is worryingly possible, especially if many AI vendors all build clinically-viable algorithms.
One Algorithm to Rule Them All
The solution is consolidation – true consolidation at the algorithm level. In radiology, each body part being scanned will require one single algorithm that combines all of the different bits and pieces and turns them into a single, unified product.
This “consolidator” will have to be a new type of AI algorithm, producing an output that’s simple for the user and highly accurate. It will probably have to be able to access more than just the raw outputs of the other algorithms; it’ll need to penetrate at least one layer deeper, into the raw data from the network itself.
The system will have two key tasks. First, it will have to aggregate the outputs of each algorithm into a single, cohesive result per imaging exam. Secondly, it must act as a context-aware filter and present information only in the right context.