Innovation that matters to you

The potential to use this wealth of information to improve outcomes is not yet fully realized. For instance, if we apply understanding risk factors of breast cancer to detailed patient profiles (which include attributes like family history or better still genetic information) across a population, we can create more personalized screening programs, which could lead to better outcomes and lower cost.

New technologies such as artificial intelligence (AI) and the Internet of Things (IoT) are gaining traction, but their most meaningful applications in healthcare today are still in their infancy. We need to be realistic about the hurdles ahead, such as interoperability of systems and devices and security to safely exchange information across and between healthcare enterprises.

Our vision is that information technology will enrich the role of the radiologists, by making them fully connected members of a networked care team, all working from a detailed longitudinal view of the patient. Radiology will link imaging to information from multiple sources to create that rich patient profile. This information becomes input for population health. Analysis of aggregated diagnostic, treatment and outcome data can help stratify patients and create personalized plans that focus on early detection and intervention.

This isn’t easy if we look at the overwhelming amounts of images that are generated by healthcare providers, in the larger ones surpassing two million images a week. New data from digital pathology, genomics, wearables and EMRs will be added to the body of knowledge that a radiologist could use to make diagnostic decisions. A complex patient case can already add up to a terabyte of data. Our ambition is to provide intelligent tools that enable the radiologist to manage all this information and make it actionable.

Let me give you some tangible examples. I am very excited that Philips is the first to bring adaptive intelligence to radiology. This is a relatively new field, combining domain specific models and knowledge with Artificial Intelligence to create an adaptive and contextual user experience, anticipating user needs and augmenting their work.

With Illumeo, one of newest solutions to launch at RSNA, we are setting the first steps applying adaptive intelligence to imaging informatics. Its built-in intelligence records the radiologists’ preferences and adapts the user interface to the clinician by offering tool sets and measurements driven by the understanding of the clinical context. Integrated with existing systems such as Philips IntelliSpace PACS the technology aims to enable faster and more accurate diagnoses and well-informed care decisions for improved patient outcomes.

Another example is advanced image visualization and quantification of images captured by Philips or 3^rd party imaging systems. It’s typically for patient cases that require deep clinical analysis. Today we are working on solutions that apply machine learning techniques to continually learn the usage patterns of users to speed up time consuming image preprocessing tasks and enhance the important daily functions of a radiologist.