Platform & software · Radiology AI platform

Radiology AI platform with one assistant instead of separate radiology AI tools

The short answer

A radiology AI platform is software that runs several AI capabilities through one integration, one governance process and one contract, instead of buying a separate point solution for each finding. The distinction matters because the FDA had authorized 1,104 radiology AI devices by the end of December 2025, roughly 76% of all AI-enabled medical devices it has ever cleared, and no practice integrates them one at a time. Platforms differ in what they unify: some are marketplaces that distribute other vendors' algorithms, while Radiological.ai unifies the working read itself, flagging suspected findings, prioritizing the worklist and drafting the structured report in one pane. The radiologist reviews, edits and signs every study.

Most groups end up running one vendor for triage, another for report drafting, and a third for finding flags. Three logins, three bills, and a workflow stitched together at the edges. The radiologist still does the stitching.

That sprawl is not a hypothetical. By the end of December 2025 the FDA had authorized 1,451 AI-enabled medical devices in total, and 1,104 of them, about 76%, were radiology devices. GE HealthCare alone holds 120 radiology AI authorizations, Siemens Healthineers 89 and Philips 50. Buying that market one algorithm at a time is how a department ends up with six contracts and no coherent workflow.

Radiological.ai brings the whole read into one place: flag, prioritize, draft, review. It surfaces suspected findings for a second look, pushes urgent studies to the top of the worklist, and drafts the structured report into your template so you sit down to edit rather than start from a blank page. You always review and sign.

The Reading Station

Assistant ready

Worklist

Not for diagnostic use

Structured report

Draft

Run the assistant to draft this report for review.

You review & sign

Decision support. The radiologist reviews and signs every study.

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

The buying question underneath this page is how many vendors a department should carry at all, which we work through in how many radiology AI vendors a practice actually needs. If you are still mapping who does what, AI radiology companies in 2026 covers the field and the vendor evaluation questions are worth sending to every name on your shortlist in the same email so the answers line up.

The individual capabilities have their own pages: radiology worklist prioritization software for queue order, structured radiology reporting for the report itself, AI triage for the urgent slice and PACS integration for how any of it reaches the reading station. To compare named vendors side by side, start at the radiology AI alternatives hub or the AI radiology software roundup.

Side by side

Three ways to buy radiology AI, compared

Point solutions, marketplace platforms and reporting-integrated assistants solve genuinely different problems. None of the three is the right answer for every department.

What you are comparing	Point solution	Marketplace or platform layer	Reporting-integrated assistant
What you actually buy	One algorithm for one finding, such as intracranial hemorrhage or pulmonary embolism	A distribution and integration layer that resells many vendors' algorithms through one pipe	One assistant that carries the whole read, from flagging through the drafted report
Typical breadth	One or two modalities and a narrow indication list	Very broad by design; CARPL.ai for example publishes 300+ applications from 100+ AI vendors	The routine worklist across X-ray, CT and MRI rather than a specific indication list
Integrations to maintain	One per vendor, so the count grows with every purchase	One to the platform, though each algorithm underneath is still a separate vendor relationship	One, covering flagging, prioritization and drafting together
Governance load under the ACR and SIIM parameter	Each tool needs its own catalog entry, local acceptance testing and ongoing performance monitoring	The catalog still lists every algorithm you switch on, so the monitoring burden scales with usage	One tool to catalog, acceptance test and monitor
Where the output lands	Usually a flag or notification back into PACS	Into whatever each underlying algorithm supports	Into the report draft and the worklist order the radiologist is already working in
Cost of adding the next pathology	A new contract, a new security review and a new integration	A line item on an existing contract, which is the main reason marketplaces exist	Not the buying model; breadth comes with the assistant rather than per indication
Regulatory status	Each vendor publishes its own FDA clearances	CARPL.ai describes its platform as FDA 510(k) cleared; each listed application has its own status	Decision support; we make no regulatory-status claims on this site
Report drafting	Rare; most point solutions stop at detection and notification	Depends entirely on which applications you enable	Drafts the structured report into your template for you to edit and sign
Best suited for	A department with one specific, high-volume clinical gap to close	Health systems that want to trial many algorithms without a new integration each time	Groups whose bottleneck is the volume of routine reads, not one missing detector

Device counts are from the FDA AI-enabled device list covering authorizations through the end of December 2025, as reported by The Imaging Wire in March 2026. Governance requirements are from the ACR and SIIM practice parameter for imaging AI approved May 5, 2026. Marketplace application counts are each vendor's own published figure. Confirm current numbers and contract terms with any vendor before building a shortlist around this table.

Why it works

What your group gets with radiology AI platform

One read, one pane

Flag, prioritize and draft live together instead of across three vendors, so the radiologist works in a single calm view rather than tab-switching between point tools.

Across X-ray, CT and MRI

The same assistant supports your full modality mix, so a group does not need a different tool for chest films, head CTs and brain MRI.

One thing to govern

The ACR and SIIM practice parameter asks every site to catalog its AI tools, acceptance test each one locally before go-live and monitor real-world performance afterwards. That work is per tool, so the size of your stack decides the size of the job.

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 findings for a second look
Prioritizes the worklist so urgent studies surface first
Drafts the structured report into your template
Covers X-ray, CT and MRI in one assistant
Keeps one tool to catalog, acceptance test and monitor
Radiologist reviews, edits and signs every study

RADIOLOGY AI PLATFORM 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.

Decision support · 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 radiology AI platform

A radiology AI platform is software that delivers more than one AI capability through a single integration, contract and governance process, rather than a separate product per finding. In practice the term covers two different things: marketplace platforms that distribute other companies' algorithms, and assistants that unify the read itself by flagging, prioritizing and drafting in one place. Ask which kind you are being quoted for.

A point solution detects one thing, such as a large vessel occlusion, and hands you a flag. A platform carries several capabilities through one pipe. The practical difference is not clinical quality, it is overhead: each point solution brings its own contract, security review, integration and monitoring obligation, so the administrative cost grows with every vendor you add.

Fewer than most vendor conversations imply. Start from the bottleneck rather than the catalog. If cases sit unread and reports take too long, breadth across the routine worklist matters more than another detector. If one specific pathology is being missed or delayed, a single strong point solution aimed at it will beat a broad platform. Most groups need one workflow tool and, at most, a couple of targeted detectors.

By the end of December 2025 the FDA listed 1,451 AI-enabled medical devices in total, of which 1,104 were radiology devices, roughly 76% of everything it has authorized. Radiology Business reported that the FDA's June 16, 2026 update added 68 more radiology algorithms cleared in the first three months of 2026. The list grows faster than any department can evaluate it, which is the real argument for consolidating.

The ACR and SIIM approved the first practice parameter for imaging AI on May 5, 2026. It asks facilities to stand up an AI governance structure with clinical, technical and compliance leadership, keep a catalog of every AI tool in use with versions and intended purposes, run local acceptance testing before anything goes live, and monitor real-world performance afterwards with predetermined stopping criteria. A practice parameter is guidance rather than law, but it is the standard your peers will be measured against.

No. Radiological.ai is decision support. It flags regions for review, prioritizes the worklist and drafts the report, but the responsible radiologist reviews, edits and signs every study. It does not provide a diagnosis.

No. The platform is built so flagging, worklist prioritization and structured report drafting live in one assistant across X-ray, CT and MRI, instead of stitching several vendors together.

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