Maryland's Eliot Siegel discusses AI's potential for disruption in medical imaging

Implementing artificial intelligence (AI) and machine learning into clinical practice is a primary topic for debate for healthcare. At this year’s SIIM Conference on Machine Intelligence in Medical Imaging (CMIMI) in San Francisco, industry leaders came together Sept. 9 to discuss the impact and disruption of AI in medical imaging and healthcare.

Moderating the panel was Eliot Siegel, MD, a professor of radiology at the University of Maryland School of Medicine and chief of imaging services at the Veterans Affairs Maryland Health Care System. Health Imaging spoke with Siegel before the panel to learn more about challenges and opportunities related to artificial intelligence (AI) in medical imaging, how AI and machine learning should be incorporated into clinical applications, and what the future holds for AI in imaging.

Health Imaging: How has the University of Maryland School of Medicine and the VA used AI and machine learning in medical imaging and clinical applications?

Eliot Siegel, MD: I think overall we have taken very little advantage with healthcare—not because of the lack of interest but because applications related to deep learning have, for the most part, not been available to us. We are not using convolutional [neural] networks or machine learning, and a lot of the things that we get as far as dashboards and reports are based on relatively simple descriptive statistics rather than utilizing predictive models. Healthcare tends to be 15 to 20 years behind some other areas in the industry, and I think that most of those tools at this point are not available to [those of us] who work in healthcare. Even in 2018, there are disappointingly few real-world applications that are available to me to implement and I'd love to see that change.

How has the lack of having AI and machine-learning technologies at the University of Maryland and the VA impacted patient care?

Patient care, in many ways, is suffering because of the fact that, despite our transition from a paper to digital environment, electronic medical records are still just digital versions of paper. The electronic medical records don't support a type of simple query, much less support predictive analytics or models. Healthcare is so far behind, it’s almost as though when I walk through the doors of the hospital or an outpatient center I'm walking backwards in time to an era where there were far more limited capabilities. With electronic medical records, I can't do so many of the things that I want to do to be able to improve patient care, to be able to improve communication, to be able to improve safety. I think we’re on the cusp of being able to start to do that and certainly machine learning and specifically deep learning offers the potential to be able to take that to the next level.

What is challenging about implementing machine learning and AI into healthcare?

I think part of it is the culture; healthcare systems tend to be very slow to adopt new software.  Another part of it is that a lot of the folks who have been the stewards of information technology within healthcare were traditionally—before the transition to the electronic medical record—more focused on billing and lab results and weren’t really data scientists.

Another part of it is that the larger electronic medical record companies have been focusing more on just getting their software out and running. Look at a lot of the major added value related to not just billing, but real clinical predictive models and analytics, which is where I think we should go next. We’ve all rushed really quickly into an electronic medical record environment in order to be compliant with regulatory requirements. I think now we're trying to figure out how within the constraints of the larger systems—Epic and Cerner, for example—we can add these types of advanced machine learning algorithms to all sorts of different applications.