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The future of AI in radiology

November 22, 2019
Artificial Intelligence X-Ray
Kees Wesdorp
From the November 2019 issue of HealthCare Business News magazine

By: Kees Wesdorp
Imaging is the diagnostic backbone of healthcare, and around the world, radiologists are having more impact in value-based care, helping to accurately diagnose more patients more efficiently than ever before.
It’s a great achievement, but even as we take a moment to recognize this success, it’s clear that even more is being asked of radiology — and we have more to give.

Faced with aging populations, increasingly complex patients with multiple chronic diseases, and a relentless pressure on costs, healthcare systems are increasingly under strain: what we’ve done in the past isn’t going to get us where we need to go tomorrow. The North Star guiding our innovation in radiology is to enable precision medicine: treatment that’s both appropriate and as unique as the patient receiving it.

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Diagnostic imaging is poised to play a central role in the first part of the precision medicine challenge — precision diagnosis. It’s an approach founded on combining insights from all the available data; longitudinal view of radiologic, molecular- and anatomic-pathology, and clinical findings. The more accurate and specific we can be in diagnosis, including identifying those who may be at higher risk of certain conditions from the outset, the more effectively and cost-efficiently we can treat the condition or disease.

Artificial Intelligence (AI) is the key enabler for achieving precision diagnosis. Today, we can have an incredible amount of data on a patient, but too often that data remains in silos and in different formats. To get meaningful insights from that data, it needs to be standardized and combined into a holistic view of the patient. And by applying AI effectively in the context of user needs, we can translate that data into actionable insights.

We’re already harnessing the power of AI to benefit patients and clinicians across the imaging department. In MR, AI is supporting automation of the planning, scanning and processing of exams, helping to improve the entire MR workflow, from image acquisition to reading preference. In ultrasound, AI is helping to quickly and consistently segment the chambers of the heart and quantify cardiac function. In informatics, AI is anticipating what a radiologist needs to do next, enhancing their expertise and efficiency, and helping to standardize workflow and reduce variability.

As the science fiction writer William Gibson famously said, “The future is already here, it’s just not very evenly distributed.” There are many examples of AI already making a difference in radiology. But with so many vendors — startups, medical technology companies, IT companies — the challenge for healthcare providers is to capitalize on the opportunity of AI without getting caught up in its complexity. At Philips, we believe that the future of AI in healthcare will be enabled by open platforms, through which many AI assets can easily be managed and deployed to maximize the operational and clinical potential of the technology.

Integrating AI across the healthcare enterprise goes hand-in-hand with taking a value-based care approach. Only by changing how health systems are organized, financed and regulated can we achieve better health outcomes, improve the patient and staff experience, and lower the cost of care.

If we can successfully harness the power of digitalization and AI, we have the potential to realize the promise of precision diagnosis and value-based care. And for the radiologist, this future offers the potential to free up time to spend on the more rewarding parts of their jobs, focusing on the most challenging diagnoses and engaging with their peers to ensure the best possible outcome for each patient.

About the author: Kees Wesdorp is the business leader for diagnostic imaging at Philips.

Archil Kapanadze

AI in Medicine

November 25, 2019 06:11

Radiology is an Anatomy in live. Are you sure that holistic approach can be based on Anatomy? As it seams, your approach is - as many data we have more precise diagnosis we can have. Absolutely no. Future of medicine is in functional diagnosing which needs new theory based on the description of health.

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