Medicai's cloud PACS gives practices a secure platform for bringing AI tools into everyday imaging workflows. Connect AI tools to the studies your radiologists already read, so results are available where clinical decisions are made.
Medicai's AI-powered diagnostics combine our cloud PACS and DICOM viewer with the AI tools a practice chooses to use. The platform stores and routes imaging studies, connects them to AI tools for analysis, and presents the results alongside the images so radiologists can review them in their normal workflow.
The aim is practical: give radiologists access to AI support where it helps, without adding separate systems or logins. Interpretation and clinical decisions remain with the radiologist.
Medicai's cloud platform supports the deployment of AI tools that analyze X-ray, MRI, and CT images, so their results are available alongside the studies in your workflow.
Through the Medicai Imaging API, practices can connect imaging with the electronic health record and other clinical systems, so radiologists and referring physicians see studies in the context of the patient's care.
Medicai's Radiology AI Co-Pilot turns a radiologist's dictation into a structured report draft inside the DICOM viewer. Radiologists review, edit, and sign every report.
Used well, AI can help radiology teams work more consistently and spend more of their time on interpretation.
AI tools can help radiologists review complex studies more consistently. Some of the strongest evidence comes from breast screening: in the MASAI randomized trial in Sweden, AI-supported screening detected more cancers than standard double reading by two radiologists, while substantially reducing reading workload. These results come from AI working alongside radiologists, and performance varies by tool, scanner, and patient population, so each tool should be evaluated before use.
When AI results appear alongside the images in the viewer, radiologists can review them as part of their normal read rather than switching between separate applications.
AI-assisted reporting turns dictation into structured report drafts, reducing the typing and formatting that slow down reporting.
A single cloud platform for storing, routing, viewing, and sharing studies reduces manual transfers and lets teams spend more time on patient care.
Medicai provides the imaging infrastructure that AI tools depend on: reliable access to studies, secure storage, and a viewer where results are reviewed.
Medicai's cloud PACS connects across sites and organizations, retrieving imaging data from modalities, existing PACS systems, and patient uploads, so the studies AI tools and radiologists need are available in one place.
Our interoperable infrastructure offers scalable, fast storage for medical images. The platform is built to support HIPAA and GDPR requirements, and granular access control lists (ACLs) let practices manage exactly who can see each study.
Multi-organization accounts support collaboration among healthcare professionals. Users view and analyze studies in our diagnostic viewers, including FlexView, and the Medicai Imaging API connects imaging to other systems for sharing and collaboration.
Give it a try, play with it! Using our embeddable DICOM Viewer, you can easily view your DICOM files anywhere online (web, in the mobile application). Your DICOM files are stored in your Medicai workspace, in your cloud PACS.
"With Medicai's intuitive platform, I can seamlessly share cases, annotate images, and facilitate discussions, enhancing learning and collaboration within our community. It's truly a game-changer in advancing radiology education."
Sorin Ghiea
We designed our platform to integrate with existing healthcare IT systems, so AI tools can be brought into current radiology workflows without replacing the systems teams already use.
Medicai provides a stable, scalable cloud platform for connecting AI tools to existing radiology workflows. AI tools evolve, and updates should happen under the practice's control: the 2026 ACR-SIIM Practice Parameter for Imaging AI recommends that practices track model versions, receive notice before updates, and test performance on their own data before and after changes.
A stable imaging platform underneath makes that kind of governance easier to apply.
Is AI better than humans at radiology?
AI and radiologists have different strengths. AI can analyze large volumes of images quickly, quantify findings, and perform repetitive tasks without fatigue. Radiologists integrate clinical context, manage complex cases, and make nuanced judgments. The best evidence so far shows AI improving results when it works alongside radiologists, not instead of them.
Will AI take over radiology?
That is unlikely. Radiologists do more than interpret images: they provide clinical insight, advise on next steps, and take part in patient care decisions. AI is a tool that supports radiologists, and both practices and radiologists remain responsible for the care delivered when AI is used.
How AI is used in radiology?
AI in radiology is used to help detect and measure findings on images such as CT, MRI, and X-ray, to prioritize worklists, and to assist with reporting. These tools support the radiologist, who reviews the AI output and makes the final interpretation. Performance varies between tools and settings, so practices should test AI tools on their own data.
Seamlessly retrieve, view, store, and share medical imaging data with a robust multi-location, cloud PACS storage, zero-footprint DICOM viewers, AI support, and best-in-class sharing capabilities.
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Medical ImagingPET CT ScanMRI ScanCT ScanMammogramsAngiographyMR AngiographyUltrasoundDEXA Scans
