AI for radiology reports
AI for radiology reports has to be a clinical production tool, not a promise of replacement. The radiologist stays in control of the diagnosis. This guide shows when AI makes sense in the reporting routine, which signs of maturity to look for and how to evaluate an assistive tool without compromising medical review or data governance (LGPD/ANPD). It is important to separate two families of AI that are often confused: image-detection AI (CAD, triage, quantification), which operates on the exam's pixels, and report-generation AI, which operates on the language and structure of the report. This guide covers the second — the one that turns the radiologist's reasoning into structured, reviewable, signable text — and the safety, authorship and governance criteria it requires under CFM Resolution 2.454/2026.
Framing and responsibility
Informative and assistive content. Laudos.AI speeds up the report's structure; the radiologist reviews, edits and signs. Responsibility for the report remains with the physician.
Assistive use, under the radiologist's responsibility (CFM Resolution 2.454/2026). Data processing in accordance with LGPD/ANPD.
When it makes sense
- Speeding up the routine without losing safety
- Organizing findings and impression
- Standardizing language across teams
- Reducing format variation in large teams and on-call shifts
- Sustaining an audit trail between suggestion and signed report
What needs to improve in the routine
AI only adds value when it reduces rework without hiding findings, without weakening medical review and while keeping the integration with PACS/RIS. In medical reports, the output has to be structured and reviewable: technique, findings, impression and classifications must be clear for the radiologist to confirm, correct or reject before signing.
Signs of maturity
A mature AI for reporting is transparent about its limits and keeps the radiologist in command. Look for:
- Explains the model's limits and the points of medical review
- Records the change history before signature
- Distinguishes style personalization from base training
- Keeps structured output without hiding clinical uncertainty
Clinical criteria before productivity
In healthcare, productivity comes after safety. Before measuring time gained, assess whether the tool preserves medical authorship, separates suggestion from decision, records changes and makes it evident when a passage was generated, edited or accepted.
The best test is not a perfect demo case. Use real, curated exams, compare the final signed report with the suggested version and record where there was correction, omission, overconfidence or real review gain.
- Responsibility: the physician reviews, edits and signs the report
- Traceability: changes remain auditable before release
- Consistency: language and classifications follow the service's standard
- Privacy: data processed in accordance with LGPD/ANPD and institutional policy
Image-detection AI vs. report-generation AI
The two kinds of AI coexist in radiology, but they solve different problems and demand different care. Confusing them leads to wrong expectations — and to poorly addressed governance risks.
Detection AI acts on the image: it flags, measures or classifies regions of the exam, and its typical error is the false positive or false negative at the pixel. Report-generation AI acts on language: it structures technique, findings and impression from the physician's reasoning, and its typical error is undue inference, omitted uncertainty or an impression disconnected from the findings. In both cases, CFM Resolution 2.454/2026 keeps the decision and the signature with the radiologist.
- Detection: operates on pixels; output is a mark, measurement or score; risk is a false positive/negative in the image
- Generation: operates on language; output is structured text; risk is undue inference or lost uncertainty
- In common: neither decides nor signs — the physician reviews, confirms or rejects
- Governance: both need an auditable trail of suggestion, acceptance and editing before release
How to evaluate an assistive AI without falling for the easy test
The demo tends to use the case the tool gets right. An honest evaluation uses the case it might get wrong. Structure the test to expose the limits, not to confirm the promise:
- Gather real, curated exams, including long reports, incidental findings and ambiguous cases from your service
- Compare the final signed report with the original suggestion and classify each divergence: correction, omission, overconfidence or real gain
- Push the fragile points: negations, laterality, measurements in sequence, comparison with priors and diagnostic uncertainty
- Check whether the AI marks what is inference and what is transcription of the physician's dictation
- Confirm that every edit is recorded before signature, with author and time
- Repeat the set on the current baseline (typing or the previous tool) to compare against a fair reference
Warning signs: when to back off
Some behaviors are not product details — they are reasons not to adopt, however attractive the time gain. Treat them as eliminating criteria:
- The tool promises an "automatic report" or suggests skipping medical review
- There is no way to tell AI-generated text from text written or accepted by the physician
- Edits are not recorded, or the history can be erased without a trace
- Clinical uncertainty disappears: the AI turns a probable finding into a categorical statement
- Patient data travels or is stored without a clear legal basis, residency and contract under the LGPD
- Data residency and subprocessing are not disclosed in a verifiable way
How Laudos.AI solves it
Laudos.AI organizes findings, impression and classifications in radiology reports with the specialty's vocabulary, reviewable templates and critical findings inside the workflow — with LGPD/ANPD governance and mandatory human review. The AI speeds up the report's structure; the clinical decision remains the physician's.
Radiology vocabulary, templates by modality and review before signature
Critical findings flagged inside the workflow (CRIT), with acceptance and an audit trail
LGPD/ANPD governance, data flow documented per deployment and medical review preserved
Structured, reviewable output, always released after medical review and signature
Frequently asked questions
When does AI for radiology reports make sense?
When the goal is to speed up the routine without losing safety, organize findings and impression and standardize language across teams. A useful pilot measures curated clinical material, review quality, template adherence, edit traceability and integration friction.
Is the AI that generates the report the same one that detects findings in the image?
Not necessarily, and it is important not to confuse them. Detection AI acts on the exam's pixels (CAD, triage, quantification) and flags regions. Report-generation AI acts on language: it structures technique, findings and impression from the physician's reasoning. They are different technologies with different risks. In both cases, under CFM Resolution 2.454/2026, the decision and the signature remain the radiologist's.
How does the AI handle diagnostic uncertainty?
A mature AI preserves uncertainty rather than erasing it. A probable finding must not become a categorical statement, and expressions of probability or differential diagnosis need to survive structuring. In the test, deliberately check whether the tool keeps the degree of certainty the physician dictated — turning 'suggests' into 'corresponds to' is a clinical error, not a stylistic one.
Where is patient data stored?
That is a governance criterion, not a detail. The tool must disclose, verifiably, data residency, the legal basis for processing under the LGPD and which subprocessors take part in the flow. At Laudos.AI, regions, subprocessors, persistence, retention and backups are documented per function and deployment; medical review remains mandatory.
Does Laudos.AI replace the radiologist?
No. Laudos.AI structures and speeds up the report, but the physician reviews, edits and signs. Use is assistive and responsibility for the report remains with the radiologist (CFM Resolution 2.454/2026).
Do I need to change PACS/RIS?
No. The planned deployment connects to the existing infrastructure and keeps the familiar reporting flow, without forcing a change of PACS/RIS, worklist or exam data.
References
- Insights into Imaging (Bruls & Kwee) · 2020 · DOI: 10.1186/s13244-020-00925-z
- Journal of Digital Imaging (Forsberg et al.) · 2017 · DOI: 10.1007/s10278-016-9911-z
Meet Laudos.AI
Dictation in Portuguese with radiological terminology, automatic structuring, critical-finding flagging (CRIT) and integration with your current PACS/RIS. The physician reviews, edits and signs.
Content updated on .