Your entire radiology operation, on a single dashboard

The service's bottleneck is rarely the image; it is radiologist time tied up in non-clinical tasks.

Every radiologist hour has a cost, and a good part of it is consumed by formatting, typing and restructuring text, not by interpretation. The post-imaging layer targets precisely this waste. In production, we measured a median of 52 seconds from editor to signature and 54.6% of reports going out in under a minute (n=5,200 reports, 30-day window).

From this measurement, the order of magnitude is approximately 10 hours returned per 100 signed reports. That is the variable you project against your monthly volume to estimate recovered capacity, without hiring. The commercial proposal uses your operation's real volume; it is an illustrative calculation of your routine, not a promise of results.

Translated for leadership, the argument is to convert clinical hours now spent on typing into additional reading capacity, backlog reduction or slack in the schedule. The return shows up in volume per radiologist and in TAT, two metrics that procurement and clinical leadership already track and know how to defend.

You need to size the team and balance the load, not rank people.

The dashboard shows output by queue and by radiologist, TAT by modality at P50 and P90, backlog and queue aging. This exists to answer management questions, where the queue is aging, which modality breaches the SLA, when the schedule needs reinforcement. It is an instrument for sizing and balancing, not for surveilling the clinical staff.

The distinction matters politically. An output metric becomes a pressure tool when it is used to rank individuals, and becomes a management tool when it is used to redistribute the queue, anticipate reinforcement and protect those who are overloaded. The platform's positioning is governance and visibility, with the physician as the author of the report, not as the target of a performance audit.

A critical finding is only resolved when someone who can act confirms receipt.

The CRIT module treats the critical finding as a process with SLA, escalation and confirmation of receipt, not as a phone call nobody recorded. The loop only closes when there is confirmation from someone who can act on the patient. This follows the logic of ACR Actionable Reporting, closed-loop communication through to effective confirmation of receipt.

For management, the value is in the trail. Each critical finding has a record of when it was communicated, to whom, how quickly and with what outcome, which turns a long-standing point of clinical and legal risk into an auditable process. When the SLA is not met, escalation is triggered, and the event is documented per exam, available for audit.

Compliance is a prerequisite for signing.

Laudos.AI has a designated DPO and role-based access controls. Audit events, trail content and retention depend on the module and the deployment. The DPO and the legal team must validate the sensitive-data flow in the local context.

Regions, subprocessors, processing, persistence, logs, retention and backups are documented separately. Some subprocessors may process data outside Brazil. The documentation supports, but does not replace, the institution's due diligence.

The resolution does not prohibit AI; it requires meaningful human supervision, and that is how the platform operates.

CFM Resolution 2.454/2026 establishes that AI systems in medicine operate under meaningful human supervision. In service practice, this means the AI is assistive, it structures the report and suggests, and the physician reviews, edits and signs each document. Authorship belongs to the radiologist, and responsibility for the content remains with whoever signs.

What changes for the service is less than the director fears and more than marketing usually promises. The platform does not issue autonomous diagnoses or replace the radiologist; it speeds up the production of the text and standardizes the structure (Technique, Analysis, Impression and, when applicable, Classification). The signature flow is compatible with the regulatory requirement from the start, with no improvised extra step.

This has risk-management value. A service that adopts AI needs to demonstrate, in an audit or when questioned, that there was effective human review, and the per-exam trail documents exactly that. You gain productivity without opening a regulatory flank, because meaningful supervision is in the product's design, not in a policy nobody follows in the routine.

You decide with numbers from your own operation, not with another service's case study.

The evaluation is a 30-day pilot with metrics, not a migration. Integration is carried out by a dedicated engineer, starts in a test environment with synthetic or anonymized data and works with what you already have, HL7 v2 (ORM/ORU), FHIR R4, DICOM-SR, DICOM MWL (C-FIND), PACS, RIS and worklist. Nothing that works today is replaced.

In the pilot, you measure what matters for the decision: TAT at P50 and P90 by modality, time from editor to signature, rate of reports under one minute, rework and addenda, and output per queue. At the end, the decision comes from data from your routine, not from promises. No contractual lock-in: if the numbers don't add up, you don't continue.

Losing a radiologist costs more than any software license.

Radiologist burnout and turnover are a management problem, not a matter of vocation. A significant part of the fatigue comes from the repetitive work of structuring and typing text, exactly the step that the post-imaging layer reduces. Giving back clinical time and cutting the mechanical part of the report tackles one of the causes of exhaustion that leadership sees in the turnover bill.

Standardized structure and TAT visibility also make it possible to redistribute the queue with clear criteria. Instead of letting overload concentrate on whoever reports fastest, the manager sees queue aging and the workload per radiologist and rebalances before it overflows. This protects retention, because the perception of an unfair workload is one of the silent drivers of departures.

What the tool does not do: It does not solve staffing on its own or replace scheduling decisions; it provides the data and gives back the time so that the management decision becomes possible. The productivity gain only turns into retention if the recovered slack is used to relieve workload, and not just to demand more volume from the same team.