Monthly Archives: July 2026

AI Reimbursement Is Becoming a Workflow Problem

CMS has proposed a new way to recognize and potentially pay for certain software-based clinical services (always start with the fact sheet – far more readable than the full text). This may matter in radiology, but it does not mean every AI tool will suddenly be reimbursed by Medicare.

The proposal calls these tools Software as a Medical Service. Examples include software that extracts useful information from medical images, such as heart blood-flow analysis, fracture-risk scores, or brain MRI comparisons. CMS would give some of these services their own payment category while it learns more about how they are used.

Medicare is beginning to acknowledge that some software can provide clinical information beyond a simple workflow aid, and it augments rather than replace physician work (Radiologists have been saying this for a while, and other -ologies expected to follow as product lines expand). Still, the proposal is temporary and limited. Many AI tools would not qualify for separate payment, and payment will depend on appropriate ordering, documentation, billing, and medical necessity.

Clear language matters – CMS differentiates between assistive, augmentative, and autonomous. Software that helps a radiologist work faster (assistive) is different from software that provides new, clinically useful measurements (augmentative). Both differ from a system that makes a diagnosis without a clinician (autonomous). Administrative tools such as scheduling or drafting messages can be useful, but they are not diagnostic services.

The bigger challenge is workflow. Buying or building a tool is only the beginning. All of it involve some form of cost. Hospitals must integrate it into their systems, review privacy and security, train staff, check its performance, document its use, bill correctly, manage denials, and show that it improves care. On the flip side, a billing code alone does not guarantee revenue.

For radiology leaders, the practical next step is to take inventory: Which software tools are already in use? What do they add to patient care? Are eligible services being documented and billed correctly? What do they cost, and what clinical decisions do they improve?

CMS’s proposal is a meaningful step, but not a windfall. The opportunity is to identify the software services that provide distinct value, and build reliable workflows around them.

“The Moon is Orange” – When the Air Gets Visible

On the way to summer camp yesterday morning, my kids pointed to the sky and said “look the moon is orange!”

The strange thing about bad air is that it is usually abstract until it is not. It turns out, that was the sun at 8 am on a ‘sunny day.‘ There was so much ash in the air that you could directly stare at the sun with the naked eye, even mistaking it as the moon.

On Thursday morning, Cleveland’s sky made air quality very concrete. The sun looked filtered, the horizon was soft, and everything had that slightly apocalyptic orange-gray cast that we have now learned to associate with Canadian wildfire smoke.

A dim orange sun over Cleveland streets through heavy Canadian wildfire smoke.
I took this photo from my car at 8am. Smoke from Canadian wildfires dimmed the Cleveland sky on July 16.

And so I wondered if we could turn this situation into a little project of our own.

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FDA AI Guidance and the Hard Part of Transparency

The least glamorous part of AI in radiology may turn out to be the most important: telling people what changed.

That sounds simple. It is not. A diagnostic AI tool may be trained on one dataset, validated on another, deployed inside a PACS or reporting workflow, monitored after release, and then updated when the model, threshold, input, interface, or intended environment changes. Somewhere in that chain, a radiologist is expected to decide whether to trust a box, a score, a contour, a triage flag, or a sentence.

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AI Readiness as an Educational Obligation

I wrote a guest editorial in Academic Radiology about AI readiness as a practical educational obligation, rather than an optional informatics side quest.

The piece responds to a study of medical undergraduates and radiology trainees in China, but the issue is broader: enthusiasm and exposure do not equal competence. Radiologists need enough AI literacy to recognize workflow fit, automation bias, governance gaps, and the limits of machine suggestions. Most trainees will not become model developers.

They still need to become safe, skeptical operators who can use AI with confidence and accountability in real clinical environments, worldwide, across diverse resource settings today.