Automating medical reports
Anyone looking for a platform to automate medical reports — or a service that makes radiology reports easier to create and faster to finish — finds two kinds of promise: one that automates the operational part of the report, and one that promises to automate medicine. The first exists and is measurable. The second is not allowed in Brazil — and buyers should know that before the demo.
What can be automated today
In the radiology reporting workflow, legitimate automation acts on the steps that are not clinical decisions:
- Structuring the dictation. The physician speaks findings in natural language and the text organizes itself into technique, analysis and impression, in the institution's template — without dictating punctuation, headers or transitions.
- Terminology and standard. The same finding comes out with the same term, across shifts and across services, with a controlled vocabulary.
- Classification suggestion. Systems such as BI-RADS, TI-RADS and PI-RADS can be suggested with the reference and version of the criterion — always for the radiologist's review.
- Critical finding communication. Once the physician confirms the finding, communication proceeds with an SLA per level and every step recorded.
- Return to the RIS and the PACS. The structured report travels via HL7, FHIR or DICOM-SR, with no retyping.
What CFM Resolution 2.454/2026 requires to stay human
The first Brazilian resolution specifically about AI in medicine is explicit: a support system suggests; the physician reviews, edits, validates and signs. Autonomous image interpretation, autonomous diagnosis and autonomous report release are not automation — they are the practice of medicine, and they stay with whoever holds a medical license. A serious platform treats that limit as architecture, not as a footnote: an audit trail per event, preserved authorship and demonstrable supervision.
Where the report's time really goes
Any model can write a first draft. The physician's time goes into review: fixing text that did not come out the way they would sign it. That is why the honest metric of automation is not "reports generated" but the time from editor open to signature. At Laudos.AI that median is 52 seconds, measured in production — the event, the window and the N are published in the metrics methodology.
How to evaluate a report automation platform
- Native compliance: CFM 2.454/2026 and LGPD handled in the product design, with the data flow documented per deployment.
- Audit trail: who did what, when — including what the AI suggested and what the physician changed.
- Real integration: a round trip with RIS/PACS validated in your environment, not promised on a slide.
- Contingency: what happens when the AI is wrong or unavailable, defined before production.
- Published metric: distrust any number without a methodology. Ask for the measured event, the window and the N.
Frequently asked questions
Can the whole report be automated?
No. The operational part — structuring, terminology, suggested classification, communication and return to RIS/PACS — can be automated. Interpretation, decision and signature belong to the physician, as required by Resolution 2.454/2026.
Is automating medical reports legally safe?
It is, when the system is assistive, keeps an audit trail and preserves medical authorship. The risk lies in tools that release text without review or cannot show who validated what.
How much time does automation actually give back?
It depends on the exam mix and the accumulated personal material. The number we publish is the median of 52 seconds from editor to signature, with window and sample described in the methodology. A real seven-month case is in 1,048 reports in one month.
See also: radiology reporting software · converting images into reports · the platform's modules · HL7, FHIR and DICOM integrations.
Content updated on .