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We know the incredible value, utility and unlimited applications of 3D printing to healthcare. However, we must also acknowledge it’s place in relation to other emerging technologies that compliment and potentially outpace the possibilities of a printed object. One does not eliminate another but provides balance for the variance in users and use cases. Think about computers vs. paper or digital cameras vs. film. Everyone said computers would eliminate the need for paper but we all know that isn’t completely true. Same for cameras and many other areas.
3D Printing has endless potential of creating personalized, patient specific healthcare yet it is limited to a single use and has less runway than say, holography or artificial intelligence for the simple visualization use case. 3D Printing has more value in the areas of surgical templates, implantable devices and patient specific prosthesis which is something you can’t do with holography. Sometimes you must have the physical media. As such, 3D Printing holds value and, like paper and computers, we have both mediums and always will.
Our 3D Printing Portal (which was quite disruptive this past year) brings DICOM to print on the same technology that also runs our Holography portal, allowing users to stream imaging with incredible speed. There are many benefits of having holography or augmented reality as a complementary technology, including>
TeraRecon's HoloPortal allows you to have real time collaboration between clinicians and experts from anywhere in the world. Images stream and communication is live without delay or the need to transfer files. The great part about having holography or augmented reality as a complementary technology is that there is sweeping potential to overlay innovative AI technology. Beyond collaboration and teaching, the use of augmented reality with medical imaging spans from image review and reporting to patient overlay and communication for best practices.
There’s already discussion about the next generation of Microsoft’s Hololens having the ability for eye tracking – to see what a physician is looking at, how they’re using an image – when combined with artificial intelligence, our platform can begin to learn physician behavior and make suggestions. We will help achieve the next level of understanding about how humans interpret and interact with medical imaging.
In terms of Machine Learning, we are already using our own cloud-based platform to automatically prepare, identify and categorize medical images to allow for improved workflow and efficiency among clinicians. When you combine the use of these technologies, one does not eliminate the other but rather, creates exponential possibility for their future use.
And while we show a cloud for these solutions, we know that not all healthcare systems prefer or execute well on a cloud based model. The great part about our Machine Learning platform is the ability to have it live in “the cloud” or have it sited within a health system’s infrastructure.