Diagnostics & imaging AI · Radiology reporting
Rad AI
Rad AI is a radiology reporting product that helps groups draft impressions and keep follow-up language consistent. Radiology practices use it to speed reports while governing edit quality. It is not triage or care-coordination AI for acute pathways.
Strong fit
- Radiology groups that track report turnaround and how consistent impressions and follow-up wording are
- Health systems with time for PACS and RIS connection work and radiologist champions
- Leaders who will track how much radiologists edit, not how impressive the demo was
Weak fit
- Groups with no radiology IT owner for changes to reporting
- Buyers looking for stroke or triage coordination rather than help writing reports
- Groups unwilling to keep radiologists reviewing AI-drafted text
Bottom line
Rad AI is worth using for radiology groups that want AI help with reports and impressions and will measure report quality and turnaround honestly. It suits groups that can manage PACS integration and radiologist habits. Buyers wanting triage or care coordination should look at Aidoc or Viz.ai.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Rad AI focuses on helping radiologists write reports and related text. That is a different purchase from triage platforms like Aidoc or hospital care coordination tools like Viz.ai.
Its results are more convincing when groups track how consistent impressions are and how much radiologists edit, rather than ending a pilot on enthusiasm.
Buyers should keep their questions on report quality, consistency, and how much editing it takes.
Fitting into PACS, RIS, and reading room habits decides whether the tool sticks. That work means helping radiologists change how they work.
A model that adds clicks loses, even if the text looks good in a demo. Pricing is custom for each organization.
Among the imaging AI tools we review, Rad AI scores close to Viz.ai, below Aidoc's broader platform, and above tools that speed up scans like Subtle Medical.
Rad AI was founded by radiologists. Doktor Gurson is co-founder and chief executive, and Jeff Chang, MD, is co-founder and president. Its products are Rad AI Reporting, Rad AI Impressions, which drafts the impression section of a report, and Rad AI Continuity, which helps make sure patients with possible new cancers get their follow-up care. The company says health systems and radiology practices representing more than half of the US radiology market use its products.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Jacobian | 7.8 | 7.2 |
| Rad AI | 7.7 | 7.2 |
| Sirona Medical | 7.5 | 6.9 |
| New Lantern | 7.2 | 7.2 |
| RADPAIR | 6.9 | 7.5 |
Pricing
| Item | Detail |
|---|---|
| Model | Radiology AI contracts for large organizations, usually priced by site or volume. |
| What usually drives cost | Imaging types and sites covered, connection services, and radiologist training time. |
| What to ask before you buy | Estimated cost per site or study volume, with editing and turnaround measures agreed before expanding. |
| Published pricing | Rad AI does not publish prices. Expect a custom quote, and confirm the total cost at your volume. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Rad AI to function | |
| A radiology IT owner for changes to reporting in PACS and RIS | Reporting AI without an IT owner stalls in the reading room. |
| Radiologist champions for impression and follow-up wording | Peer habits decide whether AI-drafted text sticks. |
| Required radiologist review of AI-drafted text | Unreviewed text is a quality and legal risk. |
| Starting numbers for report turnaround and editing | An impressive demo is not a result. |
| A way to connect to your reporting system | Separate screens lose to tools built into daily work. |
| What will maximize your value | |
| Track editing and impression consistency by imaging type | Overall scores hide which worklists fail. |
| Start only with imaging types the model handles well | Big rollouts create noisy feedback. |
| Keep reading room training short and repeat it | One kickoff session is not enough. |
| Work with quality and peer review committees early | Late reviews pull tools after go-live. |
| Pause expansion when editing goes up | A forced rollout teaches distrust. |
| Deal-breakers | |
| No radiology IT owner for changes to reporting. | |
| You mainly need triage or stroke coordination (a job for Aidoc or Viz.ai). | |
| Radiologists will not review AI-drafted text. | |
| There is no date for connecting to PACS and RIS. | |
| You cannot measure report turnaround or editing. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | From signing to kickoff | 3-8 weeks for security review, PACS and RIS contacts, and choosing imaging types |
| 2 | From kickoff to first use | 8-16 weeks until reporting help is live on a small set of imaging types |
| 3 | From first use to steady results | 3-6 months of reducing edits before wider expansion |
Leadership
Recent news
- July 2026: Rad AI hired Leonard Law, formerly of Google Cloud, Coinbase, and Primer, as chief product officer.
- June 2026: Yale New Haven Health System began deploying Rad AI across more than 16 outpatient imaging centers and five hospital campuses that handle more than 700,000 radiology exams a year. Rad AI says Yale's reporting efficiency rose 12% in the first week.
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.