Amsterdam, The Netherlands – Ahead of the 2018 Radiological Society of North America Annual Meeting (RSNA), Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced the introduction of IntelliSpace Discovery 3.0, a comprehensive, open platform to enable the development and deployment of Artificial Intelligence assets in radiology with the aim to support radiologists in their clinical and translational research.
AI for Radiology
“We use IntelliSpace Discovery to bring our research activities to the next level. Everybody is talking about Artificial Intelligence. We are making our own deep learning AI algorithms,” said
Professor David Maintz, Head of the Department of Radiology of the University Hospital Cologne in Germany.
We use IntelliSpace Discovery to bring our research activities to the next level. Everybody is talking about Artificial Intelligence. We are making our own deep learning AI algorithms.
David Maintz
Head of the Department of Radiology of the University Hospital Cologne in Germany
“Together with our customers we’re enabling research in adaptive intelligence with the goal to create solutions that augment healthcare professionals and improve patient care and efficiencies of care delivery, both inside and outside of the hospital,” said Jeroen Tas, Chief Innovation & Strategy Officer, Philips. “AI is the connective tissue to seamlessly integrate data and technology to enable precision diagnosis. At RSNA 2018 we’re showing how AI is laying the foundations for solutions of the future.”
The building blocks of IntelliSpace Discovery* include:
- Front End Applications – Integration with existing clinical infrastructure provides seamless access to vendor agnostic and multi-modality data-sets, and research advanced visualization tools allow data to be prepared and processed for AI training with existing AI tools.
- Study Management – Fully scalable infrastructure contains a vendor neutral research archive for structured and unstructured data, used to upload and process data.
- Machine Learning – Research data and deployment platform obtains batch processing in a scalable high-performance computing environment to support iterative development and validation.
- Clinical Research – AI assets and capabilities include multi-parametric enablement, segmentation and annotation, tumor quantification and stratification, deep learning networks and classification.
- Research Services –Accessibility to Philips R&D for a range of services from consultancy to development.
