GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK
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Future of radiology empowered by emerging technologies #esante #hcsmeufr

Future of radiology empowered by emerging technologies #esante #hcsmeufr | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

Radiology can once again situate itself at the center of patient care by embracing emerging technologies such as artificial intelligence (AI), RSNA 2018 President Dr. Vijay Rao said in her opening address. Radiologists must do their part to make this future a reality.

"We will need to embrace AI rather than fear it," Rao said. "We will need to recommit to the patient-centered model of radiology ... develop the necessary ethical and medical-legal procedures for protecting data as well as machine-learning algorithms ... [and] form cross-sector partnerships with our colleagues in other specialties."

Such efforts should be geared toward building a transformational framework for the profession -- one that will allow the next generation of radiologists to thrive, she noted. This will help shape the future of radiology into one in which "radiologists have been empowered by technology, not replaced by it."

"It is a future in which radiologists' capacity to provide improved patient care has been dramatically increased," she said. "And finally, it is a future in which we have achieved our long-standing goal of practicing at the epicenter of care, providing value to both the patient and our physician colleagues."

Transforming radiology

The rise of digital imaging, along with the lack of interoperability of systems, the shortage of radiologists, and physician burnout, in recent years has contributed to an increase in demand for value-based imaging, according to Rao, who is the David C. Levin professor and chairwoman of radiology at Thomas Jefferson University and hospitals.

AI and machine learning have emerged as a means of meeting this demand -- potentially enabling radiologists and other clinicians to harness the large amount of complex imaging data available even more effectively, she noted. Yet the rapid growth of these technologies has also spurred a sense of concern and even fear in the medical community.

"However, I don't believe AI will ever replace radiologists," she said. "In fact, much the opposite: I believe more firmly than ever that AI has the potential to enhance the profession and transform the practice of radiology worldwide. It'll allow radiologists to spend more time on initiatives that will benefit both patients and physicians."


Via Dominique Godefroy
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Algorithms begin to show practical use in diagnostic imaging  #esante #hcsmeufr

Algorithms begin to show practical use in diagnostic imaging  #esante #hcsmeufr | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline.

 

However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology.

 

There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology.

 

The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays. The goal is to help radiologists more effectively detect and track the progression of diseases, giving them tools to enhance speed and accuracy, thus improving quality and reducing costs.

 

While the number of organizations incorporating these products into daily workflows is small today, experts expect many providers to adopt these solutions as the industry overcomes implementation challenges.

 

Data dump
Radiologists’ growing appreciation for AI may result from the technology’s promise to help the profession cope with an explosion in the amount of data for each patient case.

 

Radiologists also are grappling with the growth in data from sources outside radiology, such as lab tests or electronic medical records. This is another area where AI could help radiologists by analyzing data from disparate sources and pulling out key pieces of information for each case,.

 

There are other issues that AI could address as well, such as “observer fatigue,” which is an “aspect of radiology practice and a particular issue in screening examinations where the likelihood of finding a true positive is low,” wrote researchers from Massachusetts General Hospital and Harvard Medical School in a 2018 article in the Journal of the American College of Radiology.

 

These researchers foresee the utility of an AI program that could identify cases from routine screening exams with a likely positive result and prioritize those cases for radiologists’ attention.

AI software also could help radiologists improve worklists of cases in which referring physicians already suspect that a medical problem exists.

 

read more at the original source: https://www.healthdatamanagement.com/news/algorithms-begin-to-show-practical-use-in-diagnostic-imaging


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