Radiological.ai
How it works
Radiology AI platform AI radiology assistant AI for radiologists Teleradiology software AI chest X-ray AI CT scan analysis Structured reporting Worklist prioritization See all →
Aidoc alternative Rad AI alternative Annalise.ai alternative Nuance PowerScribe alternative Qure.ai alternative Viz.ai alternative Nuance DAX alternative Lunit alternative Gleamer alternative
Pricing Blog

By modality · AI lung nodule detection

AI lung nodule detection software that flags suspected nodules for your review

The short answer

AI lung nodule detection software reviews a chest CT and flags suspected pulmonary nodules for the radiologist to confirm or dismiss, usually alongside automated measurement and comparison against the prior study. In a lung cancer screening program it is used as a second-reader prompt on low-dose CT and to carry measurements into the structured Lung-RADS report. Radiological.ai works this way as decision support: it flags, measures and drafts, and the radiologist confirms every nodule, assigns the category and signs the report.

Nodule work is repetitive and unforgiving in equal measure. A screening low-dose CT can hold dozens of candidate nodules, each one needing to be found, measured, compared against the prior study and then written into a structured report in the right category. Do that fifteen times a day and the tedium is not the hard part; keeping the same attention on study fifteen as on study one is.

Radiological.ai takes the mechanical half of that work. It reviews the chest CT, flags regions that look like suspected nodules so the radiologist can confirm or dismiss them, places the measurements, pulls the matching nodule from the prior study for comparison, and drafts the structured report into your template with those numbers already in it. What it does not do is assign the category or make the call. It flags nothing as cancer, publishes no accuracy figures, and claims no regulatory status. The radiologist confirms every nodule, sets the category and signs the report.

Last updated July 2026

The Reading Station

Worklist

SERIES 1 · AX
SLICE 24/64
SAMPLE STUDY
NOT FOR DIAGNOSTIC USE
W 80 · L 40
ILLUSTRATIVE SAMPLE

Structured report

Draft

Run the assistant to draft this report for review.

You review & sign

Illustrative sample · not a real patient study, not a diagnosis

Drafted in · you review & sign Worklist re-prioritized

Decision support for qualified clinicians. Radiological.ai does not provide a diagnosis and is not a substitute for professional judgment.

Run the assistant

Flag · prioritize · draft · you review and sign

X-RAY CT MRI BUILT WITH RADIOLOGISTS

Decision support not a diagnosis

You review & sign

Why it works

What your group gets with AI lung nodule detection

A second look on every study

Suspected nodules are flagged for the radiologist to confirm or dismiss, a consistent prompt on study fifteen as much as on study one. It is a prompt to look, not a finding, and never a diagnosis.

Measurements and priors in place

Diameter and volume are placed on the confirmed nodule and the matching nodule is pulled from the prior study, so growth comparison starts from numbers already lined up rather than from manual remeasurement.

The structured report, drafted

The report arrives in your template with the nodule table populated, ready for the radiologist to assign the category, edit the impression and sign. The category is always the radiologist call.

What it handles

Flagged, prioritized and drafted for your review

The assistant pre-reads each study, surfaces a region of interest for review, re-prioritizes the worklist, and drafts the structured report in your template. You confirm, edit and sign.

  • Flags suspected pulmonary nodules on chest CT for review
  • Places diameter and volume measurements on confirmed nodules
  • Pulls the matching nodule from the prior study for comparison
  • Drafts the structured report with the nodule table populated
  • Supports screening low-dose CT and incidental nodules on routine chest CT
  • Radiologist confirms every nodule, assigns the category and signs
AI LUNG NODULE DETECTION STAT

Region of interest flagged for review

A focal region is surfaced on the sample study for the radiologist to review. The assistant does not characterize it as a diagnosis.

Draft impression

Suspected finding flagged for radiologist review. Correlate clinically and confirm. Draft for review and sign-off.

Illustrative sample · not for diagnostic use You review & sign

Why Radiological.ai

One assistant across the whole read

Not three vendors stitched together. Flag, prioritize and draft in one calm pane, on X-ray, CT and MRI, with the radiologist signing every study.

Flags suspected findings

A second set of eyes surfaces regions of interest for review on every study, so a suspected finding is less likely to slip past late in a shift.

Prioritizes the worklist

Suspected-critical studies move to the top, so urgent reads surface ahead of routine follow-ups across your sites and shifts.

Drafts the report

A structured draft arrives in your template, ready to edit and sign. The draft saves the typing and the measuring, never the judgment.

Good questions

Questions about AI lung nodule detection

AI software can flag regions on a chest CT that look like suspected nodules and place measurements on them, which the radiologist then confirms or dismisses. It functions as a second-reader prompt rather than a diagnosis. Radiological.ai flags, measures and drafts, and makes no accuracy claims; the radiologist confirms every nodule and signs the report.
It sits between acquisition and the report. The low-dose CT arrives, the assistant flags candidate nodules and places measurements, pulls the prior study for growth comparison, and drafts the structured report. The radiologist confirms the nodules, assigns the Lung-RADS category and signs. The category assignment and the clinical recommendation stay with the radiologist.
No. It drafts the structured report with the measurements and prior comparison already in place, but assigning the category is a clinical judgment and stays with the radiologist. Anything the assistant places in the report is a draft to be reviewed, edited and signed.
Yes. The same flags and measurements apply to nodules found on routine chest CT ordered for another reason, which is where a large share of nodules actually turn up. The assistant surfaces them for review and carries the measurement into the report so the follow-up is documented rather than lost.
Some flags will be things you look at and dismiss, which is how a second-reader prompt works. That is why every flag is a prompt to look rather than a finding, and why the radiologist confirms or dismisses each one. Ask any vendor in this category how their flags behave in practice and run a shadow period on your own studies before go-live.
Chest CTs reach the assistant from your PACS over standard DICOM, and the flags, measurements and drafted report travel back to your viewer and reporting system over HL7 or FHIR. The radiologist works in the tools they already use.
Read every AI radiology question

Explore more

More ways teams read with Radiological.ai

AI chest X-ray

A second set of flags on high-volume chest films, drafted and ready.

Learn more

AI CT scan analysis

Triage and draft on CT, where minutes matter most.

Learn more

AI MRI analysis

Support across long MRI reads and the reports they generate.

Learn more
See every radiology use case See radiology AI pricing Back to the AI radiology assistant

From the blog: best AI radiology software in 2026, how much radiology AI costs, how to implement AI in a radiology workflow, and AI radiology companies.

Read more studies, with the assistant alongside you

Flag suspected findings, prioritize the worklist, and draft the structured report. You review and sign every study.

See pricing

Radiological.ai is a workflow and decision-support tool for qualified clinicians. It does not provide a diagnosis and is not a substitute for professional medical judgment.