Simple and Secure Collaboration for Radiology
Say goodbye to working alone and embrace real-time teamwork for better teaching, case discussions, and multi-center research.
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Rapmed is for Education
Turn passive lectures into interactive learning experiences. Share cases in real time with your audience, let your audience annotate findings and ask multiple choice questions - all while our platform handles up to 5,000 participants, even with hundreds on the same Wi-Fi. Complete CT scans load in seconds, even on mobile devices.

Rapmed is for Case Discussions
Instantly connect with colleagues anywhere, on any device. Share cases via a secure link that opens in a browser-based DICOM viewer and includes built-in chat and annotation for streamlined, focused discussions. Get the timely feedback you need to make confident decisions.

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Rapmed is for Research
Collect cases from multiple sites to accelerate your studies. Invite colleagues to upload cases, assign reviewer tasks, and share your case list with your reviewers. Multi-center studies have never been easier.

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How It Works
We understand that you do not have time to learn another complicated software. That’s why we’ve made it simple. Upload. Share. Collaborate.

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Works Everywhere You Do
Free yourself from the constraints of location and device. Whether at the hospital, teaching at a conference, or providing consultation from home – stay connected to your professional community whenever and wherever expertise is needed.

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Our Users
Rapmed is created by and for the radiology community. It is trusted by leading hospitals, societies and companies. Join professionals who believe radiology is stronger when we work together.

My audience loves to practice on real cases instead of static power points. And their instant feedback helps me to become a better teacher.

Reviewing cases in a secure viewer on any device allows me to provide feedback more quickly, ensuring accurate and timely treatment decisions.

What used to be a major barrier in radiology research has become an opportunity to advance our field. By working with larger, more diverse datasets, we achieve insights that are both more robust and more generalizable.