LAUDOS.AI

Written by people who sign reports

Our articles are written in Portuguese by practicing radiologists and cover what the routine actually asks: structured reporting, interobserver agreement, TAT measurement, critical-finding protocols, LGPD and the Brazilian AI regulation for medicine (CFM 2.454/2026).

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Four modules, one clinical flow

REPORT · PRODUCTION

Voice findings become a structured draft for physician review, editing, validation and signature.

CRIT · CONTROLLED PILOT

Detection depends on physician-provided findings; channels, timing and closure are validated per deployment.

GUIDE · BETA

Classification suggestions sourced from RadCommons and always subject to physician review.

LaudAI · BETA

Draft structure per modality, subject to configuration, contract and physician review.

Plugs into your existing flow

API, HL7/DICOM and Agent are different paths. Compatibility depends on system, version and connector and is validated per deployment; there is no universal integration claim.

Governance first

Medium risk, not SaMD

Assistive application under CFM 2.454/2026; no autonomous image interpretation, diagnosis, signature or release.

Data flow by deployment

Processing, transmission, persistence, logs, retention, backups and regions are documented separately.

Evidence with limits

RadCommons is the cited corpus; LaiBench is an internal controlled fidelity benchmark, not external clinical validation.

Historical production snapshot: April 9 to May 9, 2026; n = 5,200. It is not a live rolling window.

52 s
MEDIAN · EDITOR TO SIGNATURE
54.6%
OF REPORTS IN UNDER 1 MINUTE
≈10 h
RETURNED PER 100 REPORTS
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Os tempos citados nesta página vêm de medição em produção, do editor aberto à assinatura. O evento, a janela e o N estão em Metodologia das métricas.

Conteúdo atualizado em .