Will ‘Smart’ Solutions Really Transform Cardiology?

Smart technologies are often touted as the answer to some of cardiology’s greatest challenges in patient care and practice. But where does hyperbole end and reality begin with artificial intelligence, machine learning and deep learning?

Even the most skeptical cardiologists can’t help but feel the digital waves lapping at their feet. A “smart stent” with a special micro-sensor that acts as a miniature antenna to continuously monitor hemodynamic changes in the artery and transmit those data to an external reader. An AI-driven system that recently took 1.2 seconds to accurately interpret acute disease in brain CT scans, 150 times faster than humans. An algorithm that can forecast how long individual patients will remain in the hospital, their odds of being readmitted and the chances they will soon die.

“The unique changes that are occurring as information technology collides with healthcare technology are probably the most exciting thing I’ve seen in my 35 years in medicine,” says Peter Fitzgerald, MD, PhD, who is deeply involved in that change as professor of medicine and engineering as well as director of the Center for Research in Cardiovascular Innovation at Stanford University Medical Center. “The system today is archaic and needs to be disrupted, and it will be by the new players like Google and Apple and incredible IT folks who are coming on board.”

John Rumsfeld, MD, PhD, is optimistically watching too. The chief innovation officer of the American College of Cardiology is among the first to cite the smorgasbord of opportunities that has the cardiovascular community excited. Wearables and smart phones have already become mainstream devices for monitoring patients’ heart rate, blood pressure, breathing patterns, glucose levels and asthma, then uploading the data to the cloud for viewing by their physicians. Before too long, there could be an echo-cardiography smartphone app; wearables may measure heart rate variability; wristwatches will reliably predict atrial fibrillation; micro-radar sensors will detect heart and lung activity sans electrodes; and entire metabolic panels will be collected noninvasively (and remotely) through devices strapped to patients’ arms or foreheads. 

But for any of these applications to succeed, a much larger issue must be addressed: what to do with the torrent of data generated so it can be analyzed and interpreted in a way that benefits patients and physicians.

THE TRANSFORMATIVE ROLE OF AI

That’s where AI and machine learning could be transformative. As Alfred Bove, MD, PhD, professor emeritus of medicine at Temple University School of Medicine in Philadelphia and a former ACC president, points out, physicians are already overwhelmed with terabyte-size data flows from their patients. “One way to handle it is to build logic systems that will collect the data, filter it and advise the doctor based on the best and most likely diagnoses,” he says. “The next step would be to recommend a plan of action that could include medicines to be taken and images that are needed.”

Fitzgerald, who’s leading the technology charge at Stanford University, prefers to talk about the intelligence layer as “intelligence augmentation,” or IA, a term he coined. He looks to an evolution where physicians are augmented with information learned by population statistics and other algorithms. “So a 30-year-old [physician] who enters the cardiology field should be as smart a 60-year-old,” he says.

For Eric Topol, MD, director of the Scripps Research Translational Institute and author of a forthcoming book, Deep Medicine, the inherent strength and promise of AI reside in “deep phenotyping so you can understand each individual at an unprecedented level,” which means biologic and genomic along with the anatome and physiome. “But first you need to be able to assimilate and accurately interpret all that data, which is where deep learning and AI fit in so well.”

IMAGING LEADING THE CHARGE

Imaging could be one of the earliest “intelligence-based” success stories. The reason, says Rumsfeld, is that the underlying data quality of images—against which machine learning algorithms are executed—is very high, allowing for complex pattern recognition and iterative learning. “I believe in the not-too-distant future AI will pre-read cardiac CTs, MRs, echoes and probably electro-cardiographic tracings of all types,” he predicts. “It won’t replace the role of the cardiologist, who would still do an over-read. But it may be that while you can read one or two dozen studies in a day, that may double or triple with AI-supportive pre-reading.” 

Other experts in the field firmly believe that within the next decade virtually all imaging studies will be pre-analyzed through artificially intelligent machines before they ever get to the physician. AI will further enhance the review and diagnostic processes by data mining the patient’s electronic health history for salient information, allowing for a more integrated clinical-imaging approach to patient care than is now possible.

RUMORS OF PHYSICIAN OBSOLESCENCE EXAGGERATED

If technology is on the cusp of reconfiguring the healthcare landscape, where does that leave cardiologists? Will they become accessories to machines that can work around the clock without judgmental bias, fatigue of fear of burnout?

That’s highly unlikely, says Bove, who is part of a sizable group that believes there will always be a role in healthcare for “low tech,” that even the fanciest computational algorithm can’t make a patient take his or her medicine. Physicians need to be the “arbiter of all the data,” look at the big picture and how patient behavior integrates with everything else. “What will never become obsolete is the face-to-face personal encounter with the patient,” he says. “That may ultimately be the most important thing we do as physicians to improve the outcomes of healthcare.” 

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View more features from this issue:

Building Foundations to Build Better Care

Embracing AI: Why Now Is the Time for Medical Imaging

Leveraging Technology, Data and Patient Care: How Geisinger Is Interjecting Insight & Action

Bullish on AI: The Wisconsin Way: Reengineering Imaging & Image Strategy

ML’s Role in Building Confidence and Value in Breast Imaging

Matching Machine Learning and Medical Imaging: Predictions for 2019

NYU’s Daniel Sodickson on AI, Facebook and Why Faster MR Scans Could Improve Healthcare

Machine Learning 101: Simplifying It One Term at a Time