Who checks the AI in your radiology reports?

More of your radiology reports are being written by AI than were a year ago. Ambient dictation drafts the narrative, a model generates the impression, recommendations get inserted automatically. The report lands in the queue faster and reads more fluently than it used to.

Then a radiologist signs it. And the signature still carries everything it always did: the diagnosis, the liability, and the practice’s name on the result.

So here is the question every operations manager should be asking as AI writes more of the report: who checks the AI?

The report got faster. The checking did not.

Adoption is not the problem. Surveys now put physician AI use above 60 percent, and in radiology the reporting tools are genuinely good. The problem is that the checking step never changed to match.

For decades the verification of a radiology report was one thing: the radiologist’s own read, at the moment of sign-off, under time pressure. That was a reasonable design when the radiologist also wrote every word. It is a weaker design when an AI wrote the first draft and the radiologist is now reviewing output rather than composing it. Reviewing is not the same cognitive act as writing, and it is easier to skim.

The reporting layer got automated. The quality layer did not. Adoption outran verification, and the gap is where errors now live.

What a slipped report actually looks like

This is not about dramatic, obvious failures. The reports that get signed with errors are the ones that read perfectly well.

Picture a routine overnight CT. The narrative is clean and the impression is well written. In the body, the findings place a lesion in the left kidney. In the impression, the same lesion is summarized as right. Both sentences read normally on their own. At the end of a long list, the eye accepts the fluent impression and moves on. The report is signed with a laterality error that reads perfectly.

Or the impression refers to a comparison with a prior study, stable since, when no prior was ever loaded. The model produced a plausible sentence and the comparison never happened. Or a 9mm nodule is called unchanged with no baseline to be unchanged against. Or a critical finding is present in the text but sitting three lines deep where the referring physician will skim past it.

None of these look like errors. That is the point. Generative tools add a specific failure mode on top of the old ones: confabulated detail that is fluent, plausible, and not true. The text is well formed, which is exactly what makes it hard to catch on a quick read. The better the prose, the more trust it earns that it has not necessarily verified. These are the same common report errors the field has always had, now produced faster and dressed more convincingly.

The better the AI, the less it gets checked

There is a well-documented pattern in every field that uses automation, and medicine is no exception: when a tool is usually right, people stop checking it. It is called automation bias, and it is not a sign of a careless radiologist. It is a predictable response to a system that performs well most of the time.

This is the uncomfortable part. Improving the AI does not close the checking gap. In some ways it widens it, because a tool that is right 99 times trains you to trust the 100th. A report that is usually clean is the most dangerous kind to stop reading closely, and a busy list makes closely the first thing to go.

“Who checks the AI” is not a question you answer by making the AI better. You answer it by adding a layer whose only job is to check, every time, without getting tired or complacent.

The signature still owns the outcome

Whatever drafts the report, the radiologist who signs it owns it, and the group is accountable for what ships. That accountability does not transfer to a vendor or a model.

If you own the outcome, you need a way to verify it that does not depend on re-reading every word of every report at 2am. The operations manager feels this most directly: you are accountable for the quality of work produced by a tool you did not write, reviewed by people who are moving fast. That is not a position you want to hold without a safety net.

Where this bites first: outsourced and overnight reads

The checking gap is widest exactly where the stakes are highest. Preliminary reads from external and nighthawk readers already carry more discrepancy risk than daytime finals, because the reader on shift is often not the person who will own the final signed report. Add AI drafting to that handoff and you have a fast, fluent preliminary report moving through the part of the workflow with the least direct oversight.

This is the prelim to final gap, and it is where a wet read or a nighthawk preliminary can quietly carry an inconsistency into morning. The finalizing radiologist inherits whatever the overnight report got wrong, and now some of that was written by a model rather than a person. For teleradiology operations, “who checks the AI” is not abstract. It is the difference between catching a laterality flip at 3am and explaining it to a client at 9.

What checking the AI actually requires

A real answer to “who checks the AI” has a few non-negotiable properties.

It has to be independent of the tool that wrote the report. The same system that drafts a report should not be the only thing that grades it. Independent verification is the entire point. A QA layer that only works inside one dictation vendor’s product also disappears the moment a site switches tools, and right now a lot of sites are switching.

It cannot be another model guessing. If the checker hallucinates too, you have added a second slop problem instead of solving the first. Checking against clinical consistency rules (does the laterality match, does the comparison date exist, does the impression follow from the findings, are the required sections present, was the critical finding flagged) is deterministic. It either matches or it does not. That is the kind of check you can trust to be the backstop.

It has to run before sign-off, not after. A problem caught at the moment of reporting is a quick fix while the case is still open. The same problem caught in next quarter’s review is a correction, a callback, and a conversation. Errors are cheapest to fix at the source, which is also where they are cheapest in turnaround time.

It cannot cry wolf. A checker that fires constantly gets switched off, and a switched-off checker checks nothing. Precision is what keeps a quality layer in the workflow. Trust is the feature, and a tool that wastes a radiologist’s attention loses it.

In short, the layer that checks the AI has to be:

• Independent of the tool that wrote the report
• Deterministic, not another model guessing
• Active before sign-off, not a report you read next quarter
• Precise enough that people keep it on

This is what SkiaQA does

SkiaQA is that layer. It reviews every report against clinical consistency rules before it submits, independent of how the report was produced: dictated, click-built, or AI-drafted. Laterality conflicts. Missing or wrong comparison dates. Findings that contradict the impression. Critical findings that are present but buried. Incomplete sections. It catches them at the moment of reporting, so the radiologist fixes the report while the case is still in front of them.

It is deterministic by design. SkiaQA does not add another model writing your reports for you. It checks the report you already have against the rules a report has to satisfy, which means the quality layer itself is not a new source of slop. And because it runs as a gate before sign-off rather than a dashboard you read later, the manager stops being the last line of defense. The report that ships has already been checked.

None of this slows anything down. The fastest report is the one you never have to correct.

A lot of groups are changing tools right now

There is a practical reason this question lands in 2026 specifically. Long-standing reporting platforms are being retired and consolidated. PowerScribe 360 has reached end of life, and groups across the country are re-evaluating their reporting stack for the first time in years. Many are adding AI reporting in the same move.

That is precisely the moment to get the quality layer right, and precisely why it should not be welded to any one vendor. If your QA only exists inside the dictation tool you happen to use this year, it leaves with that tool. A checking layer that is independent of the writing tool is one you keep through every migration. You can change how reports get written without changing what verifies them.

Peer review checks a sample, eventually. This checks everything, now.

The traditional answer to report quality is peer review, and the standard most US groups know is RADPEER. It samples a small fraction of signed reports, retrospectively, and scores them. The ACR’s own published research has been candid that score-based peer review is poor at surfacing real outliers, which is part of why the field is moving toward peer learning.

That is the shift worth naming. Peer review asks, months later, how a sample of reports turned out. A pre-sign-off quality layer asks, on this report, right now, before it goes anywhere, is anything inconsistent. What RADPEER measures after the fact, SkiaQA is built to prevent before sign-off. The two are not in conflict. One is the metric, the other is the mechanism. This is what automated peer review looks like in practice: not a sample scored months later, but every report checked before it is signed.

”Our radiologists are careful”

They are. This is not a claim that radiologists miss things because they are careless, and a tool that implied that would deserve to be ignored. The honest position is simpler: no human process catches everything, every time, on every report, especially at the end of a long list at 2am. That was true before AI and it is true now.

What changes with AI is not the radiologist’s diligence. It is the volume of fluent, plausible text moving past that diligence, and the quiet pull of automation bias on a tool that is usually right. A deterministic gate does not doubt the reader. It removes the dependence on perfect attention under volume, so a good radiologist on a hard shift is not the only thing standing between an inconsistent report and a signature.

If your group has adopted AI reporting, or is about to, the question is no longer whether the report gets written quickly. It will. The question is who checks it before your name is on it. That layer should be independent of the tool that wrote it, deterministic enough to trust, and present on every report rather than a sample of them.

Book a demo

If you are adding AI to your reporting workflow and want a quality gate that works no matter which tool writes the report, book a demo of SkiaQA.

FAQ

Does SkiaQA replace my dictation or AI reporting tool?

No. SkiaQA runs alongside whatever you already use. It is the quality layer on top, checking the report before sign-off regardless of whether it was dictated, click-built, or AI-generated. If you change reporting tools later, the checking layer stays.

Isn’t the AI already checking itself?

The tool that writes a report should not be the only thing that verifies it. Independent checking is the point. SkiaQA is a separate, rule-based layer, so a fluent but incorrect report does not pass simply because the system that wrote it was confident.

Is this the same as peer review?

No. Peer review samples a fraction of signed reports after the fact and scores them. SkiaQA checks every report against clinical consistency rules before sign-off, so issues are fixed at the source rather than found in a later review.

What kinds of errors does it catch?

The consistency errors that read plausibly and slip through fast reviews: laterality conflicts between the body and the impression, comparison dates that do not exist, impressions that do not follow from the findings, buried critical findings, and missing required sections.

Will it slow down sign-off?

No. SkiaQA runs at the moment of reporting, so problems surface while the case is still open and quick to fix. Reports done right the first time are also the fastest reports.

Does it work with AI reporting tools specifically?

Yes. SkiaQA is independent of how the report was produced, so it checks AI-drafted reports the same way it checks dictated or click-built ones. As more of the report gets generated, an independent gate is what keeps a person accountable for what ships.