This content is part of the Conference Coverage: RSNA 2017 conference coverage and analysis

RSNA 2017 shows AI in healthcare, value-based imaging

Medical imaging professionals converge on RSNA 2017, North America's biggest healthcare conference. This year the emphasis is on AI and machine learning, and value-based care.

RSNA 2017 is highlighting artificial intelligence in healthcare and machine learning to aid radiologists and other imaging professionals in the transition to value-based healthcare.

The RSNA 2017 conference's focus comes as AI and machine learning in fairly robust forms are bursting into many industries, including healthcare and medical imaging.

Biggest North America healthcare show

As for the 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America, it is, simply put, the biggest healthcare show in North America.

Rasu Shrestha, M.D., discusses radiology topics at RSNA 2014.

The annual post-Thanksgiving event draws some 50,000 attendees annually from around the world to Chicago's McCormick Place convention center.

Don't let the full official name deter you.

This is a real conference, not stuffy at all despite the sometimes abstruse subject matter, complete with high-level educational and user panels, technology demos and a mammoth, almost frantic, exposition floor crammed with all the biggest vendors in medical imaging -- some with booths nearly the size of football fields.

RSNA 2017 not just for radiologists

And it's not just for radiologists and other clinicians who use the world's most advanced imaging hardware that ranges from ever more sleek MRI and ultrasound machines to gamma cameras used in single photon emission computed tomography.

Most of these devices also need equally sophisticated software to analyze, share and store large, big-data-intensive images not only from radiology departments but also from other image-intensive "ologies" like cardiology, oncology and pathology.

The biggest topics in medical imaging storage, retrieval and viewing revolve around VNA and PACS technologies, the latest incarnations of which flourish each year at the RSNA conference and exhibition.

Health IT growing quickly in medical imaging

That's where health IT at RSNA 2017 comes in. The IT portion of the show has been growing every year, to the point at which you're almost more likely to meet hip software developers on the show floor as you are hardware salespeople.

Back to AI and machine learning.

The official theme of RSNA 2017 is: "Explore. Invent. Transform."

What is probably the most significant and now commercially viable part of that innovation ethos is using AI in healthcare, machine learning and what Richard Ehman, M.D., president of the RSNA board, calls "deep learning."

RSNA president looks ahead

Ehman, a professor of radiology and medical research at the Mayo Clinic in Minnesota, recently told the journal Radiology Business the technologies will be the prime focus of RSNA 2017.

Artificial intelligence and machine learning continue to be subjects of huge interest among the radiology community.
Richard Ehmanpresident, RSNA board of directors

"Artificial intelligence and machine learning continue to be subjects of huge interest among the radiology community," Ehman said. "These technologies have been around for a long time, but the emergence of widely available tools to implement them has led to an explosion of potential applications."

This keen emphasis on AI, various kinds of machine learning and value-based care is reflected throughout the agenda, including the content of scene-setting keynote addresses from some of the most highly regarded thinkers and practitioners in medical imaging.

AI-value-based care links pros and cons

While RSNA 2017 also features advances in precision medicine, breast and prostate imaging, molecular imaging and new 3D printing applications for imaging and surgery, AI predominates on the roster, as does the link between AI and value-based care.

AI in healthcare can offer dramatic advantages in improving care quality in shorter amounts of time. In short, it can make imaging more efficient, and better, according to medical imaging experts. On the other hand, some in the field warn against a headlong rush toward value-based care, which some think can distract from the doctor-patient relationship and impinge on care quality.

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