AI Should Work
In The Background
Clinical AI should remove work, not create another layer of it.
There is no shortage of conversation about AI in medical imaging. New algorithms. New capabilities. New use cases. New promises about productivity.
The Central Question
Does AI actually make the clinical day easier?
Adding intelligence to a workflow is not the same as improving the workflow. If AI means another application to open, another alert to review, or another queue to manage, even useful technology can introduce new friction.
Clinical AI should do the opposite.
It should remove steps.
Reduce repetitive decisions.
Bring the right information forward.
Work quietly in the background.
That is where we believe some of the greatest potential for AI in imaging lies.
Rethinking the AI Workflow
The goal is not more AI
AI adoption is accelerating, but adoption alone is not the outcome. A radiology department can deploy multiple AI applications and still leave radiologists moving between systems, managing alerts, and searching for information.
The question is not: What can this model detect?
The question is: What work has AI actually removed?
Rethinking the ROI of AI
Much of the AI conversation starts with the model's accuracy. But consider the smaller decisions happening continuously: Which study should be read next? Who is best positioned to read it? Which priors are most relevant?
AI can help prioritize, route, retrieve, surface, and prepare—helping the workflow make better decisions in the background.
Not only: What can the AI find?
But: What can the workflow stop asking the radiologist to do?
The best AI experience may be the one you barely notice
AI is often introduced through another interface: A pop-up, a separate application, a new notification. But every additional interaction asks something from the user. Another click. Another context switch.
Much of that intelligence does not need to become another destination. Ideally, some of it may barely be visible at all.
AI should take things off the mind
Reducing clicks is important, but reducing cognitive load may be even more important. Radiologists make countless small decisions that sit outside the interpretation itself.
AI creates an opportunity to move some of those operational decisions into the workflow itself.
The goal is not to remove clinical judgment. It is to remove the unnecessary work surrounding it.
The algorithm is only part of the system
A model can perform extremely well at its intended task and still struggle to create meaningful operational value if everything around it makes adoption difficult.
Where does the data come from? Where does the result appear? Does someone have to leave their normal workflow to act on it?
The value of AI depends on more than model performance. It depends on the environment around the model.
Start with the work, not the technology
There is a simple way to resist some of the hype around AI: Start with the work.
Where is the friction today? Which steps consume time without adding clinical value? Where are radiologists switching context unnecessarily?
Then ask where AI can help. The technology becomes a means of improving the workflow rather than the objective itself.
Less hype.
More useful AI.
AI will continue to become a larger part of medical imaging. That seems inevitable. What is less certain is how visible AI needs to become in the radiologist's day.
We do not need AI that constantly reminds clinicians that AI is present. We need AI that helps the imaging operation work better.
- AI that removes repetitive decisions.
- AI that brings information forward.
- AI that reduces context switching.
- AI that supports, not becomes, a workflow.
The best AI experience may not be another destination. It may simply make everything around the radiologist work better.
That is the standard clinical AI should be held to.
Is your imaging environment ready for AI to work this way?
Making AI part of the workflow requires more than connecting an algorithm. The data needs to be available. Systems need to communicate. AI outputs need somewhere useful to go. Workflows need to be able to respond. And the environment needs enough flexibility to support what comes next.
That is what AI readiness should answer.
Not simply: Can we deploy AI?
But: Can we make AI genuinely useful without adding more complexity?
If AI, workflow, and reducing clinical friction are part of your priorities, continue the conversation with AdvaHealth at RSNA 2026.
