From the October 2017 issue of HealthCare Business News magazine
Eventually, emerging post-processing techniques will tap elements of AI and ML to combine MR’s rich anatomical data with information from other forms of imaging. This dynamic form of post-processing could, for example, potentially enhance the live fusion of ultrasound and MR for biopsy, which could potentially lead to more accurate lesion targeting and help avoid the treatment of critical structures.
One day, we may even see comprehensive mapping of patient anatomy using MR and other modalities to create a digital avatar of each patient. This digital copy, the ultimate extension of the personalized medicine philosophy, could simulate response to therapy, thereby sparing patients the physical, mental and financial rigors associated with ineffective treatment.
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Regardless of the form AI takes, the goal will be the same: to make MR exams more efficient, accurate and consistent, ensuring more reliable results each time for each patient. Helping to realize this goal will be the essential human element that is the radiologist.
(This article examines the potential benefits of employing artificial intelligence in magnetic resonance imaging. As such, it references technology that is not currently available.)
About the author: Murat Gungor is vice president of Magnetic Resonance (MR) at Siemens Healthineers North America.
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