Machine learning in medicine

The field of medicine has so far relied heavily on heuristic approaches, whereby knowledge is acquired through experience and self-learning, which is imperative in the highly variable healthcare environment. The increase in knowledge and understanding of diseases has been associated with the growth in information and data partly thanks to advances in tools that generate

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Augmented Reality: The Future of Medicine

Augmented Reality can change brain surgery thanks to powerful diagnostic platforms, revolutionize radiology, and open new doors to reconstructive surgery. Augmented Reality (AR), also known as spatial computing –a merging of digital and physical spaces,– is one of the current technology trends that, together with Virtual Reality (VR) and Mixed Reality (MR), is changing all industries, includinghealthcare and medical education. 

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Orgenesis Announces Collaboration with Digilab to Develop Industrial 3D Printing Capability for Cellular Structures and Tissues for Clinical Use

Orgenesis Inc., a developer, manufacturer and service provider of advanced cell therapies, today announced that it has entered into a collaboration agreement with Digilab Inc. to develop a live cell printing process and systems designed to automate the production of three-dimensional live cellular structures and tissues.  Under the Agreement, Orgenesis will have the exclusive rights

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European Innovation Hub to Focus on Developing and Implementing 3D Printed Electronics

More and more, we are using special industrial 3D printers, with inkjet and aerosol jet technology, to embed conductive components within our intelligent products in what we call 3D printed electronics. Items like ECG electrodes and contactless payment cards use these embedded components to perform wireless activities and readings, like measuring the frequency of a person’s heart beats

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Artificial intelligence helps to better assess treatment response of brain tumors

A team from Heidelberg University Hospital and the German Cancer Research Centre has developed a new method for the automated image analysis of brain tumors. In their recent publication, the authors show that machine learning methods carefully trained on standard magnetic resonance imaging (MRI) are more reliable and precise than established radiological methods in the

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