David Hale, Chief Commercial Officer, Diagnostic Imaging, Guerbet
Radiologists, radiation oncologists, and other medtech experts are familiar with the important, and time-intensive, steps involved when reading, analyzing and interpreting hepatic cancer images. Capture the image, detect lesions, click on each lesion, drag virtual tools across the screen for measurements, and dictate your findings into the patient report.
"When combined, radiology and AI create a clinical synergy that, over time, can reduce costs, boost productivity, and enhance patient outcomes"
Then, in a patient follow-up appointment, you conduct the entire manual process over again.
Today’s radiologists and medtech professionals face increasing pressure to do more with less. Our aging population is presenting them with more complicated diseases. Confounding the issue of increased disease diagnoses, we are experiencing a reduction in healthcare professionals qualified to diagnose and treat patients. Further, hospitals and health systems must provide access to the latest and greatest technologies to improve clinical outcomes in their facilities.
To help address the ever-increasing needs of our healthcare system, we are witnessing the emergence of innovative solutions through the use of artificial intelligence (AI). AI technologies are making their way into the radiology suite and changing the way clinicians read and report their images. When combined, radiology and AI create a clinical synergy that, over time, can reduce costs, boost productivity, and enhance patient outcomes. Treating more people in less time requires both the finesse of a human and the efficiency of a machine.
The future is not radiologists versus AI. It is radiologists who use AI versus those who don’t.
As a global leader in medical imaging, Guerbet has a responsibility to help radiologists and medtech professionals improve patient outcomes while utilizing fewer resources. Guerbet’s Watson Imaging Care Advisor for Liver, in partnership with IBM, supports liver cancer diagnostics, and utilizing CT and MRI imaging. This innovative diagnostic support tool uses artificial intelligence to automate the detection, staging, tracking, monitoring, therapy prediction, and therapy response of primary and secondary liver cancer for clinicians. Care Advisor for Liver will be compatible with the most common PACS visualization systems, making it easy to be integrated directly into the workflows of healthcare professionals.
A recent collaboration between the National Institutes of Health, the Radiological Society of North America (RSNA), and the American College of Radiology (ACR) outlines a roadmap to apply AI in medical imaging. An important goal of this roadmap is to grow an ecosystem, facilitated by professional societies, industry, and government agencies, that allows robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging.
The application of machine learning and artificial intelligence will transform the way radiologists practice medicine. However, it should be emphasized that AI will never replace the important role that radiologists play in interpretation and patient management to improve patient outcomes.
Hesham Abboud, MD, PhD, Director of the Multiple Sclerosis and Neuroimmunology Program and staff neurologist at the Parkinson’s and Movement Disorder Center at University Hospitals of Cleveland, Case Western Reserve University School of Medicine
Health Sciences Associate Clinical Professor, Dept of Pediatrics - University of California- Irvine, Director CHOC Comprehensive Epilepsy Center Pediatric Neurology & Epilepsy , Children's Hospital of Orange County