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In the world of clinical AI, there is imaging and there is everything else. 

Researchers in Future Healthcare Journal say that while there are claims from organizations that they’ve developed an AI solution that can “diagnose and treat a disease with equal or greater accuracy than human clinicians,” most of them are based on radiological image analysis. A survey of health care leaders found that while 58 percent are using AI in clinical practice, the most common applications are all related to imaging. In fact, imaging apps approved by the FDA were primarily for the field of radiology. In the Department of Defense, which has invested significantly in health care AI-related applications, the agency has created a massive imaging portfolio.  

In other words, the rest of clinical medicine has a way to go before catching up to imaging-based AI applications. “You don’t see that level of accuracy in some of the other applications of AI yet and so I think it sort of inflates expectations when you see it get knocked out of the park in a couple of specific areas,” says Michael Matheny, MD, Co-Director Center for Improving the Public’s Health through Informatics, and Associate Professor in the Departments of Biomedical Informatics, Medicine, and Biostatistics at Vanderbilt University Medical Center. 

“The problem that we see right now in the health care AI space is that there’s a ton of remarkable work going on in the basic science space, where models are becoming increasingly sophisticated, increasingly impressive and powerful, but when you take a look at what’s happening on the front lines with patients, providers and health systems, very little of that is actually being used at the bedside for the benefit of patients, providers, and society,” says Steven Lin, MD, Founder and Executive Director of the Stanford Healthcare AI Applied Research Team (HEA3RT). 

But that doesn’t mean health care executives are dismissive of other AI applications in clinical care. In fact, 93 percent of respondents in the survey of health care leaders above say AI is essential to their strategy. Another survey, from Optum, found that while only 20 percent are in late stages of deploying AI, a whopping 98 percent of health care leaders either have an AI strategy in place or are planning to create one. Nearly 40 percent are interested in using AI to accelerate research for new therapeutic or clinical discoveries.  

In part one of our two-part series on clinical AI, Health Evolution looked at the barriers preventing wider adoption of AI in clinical settings. In part two, we examine the most promising clinical areas for AI usage that go beyond imaging.  

Primary care  

Lin’s HEA3RT team is undergoing a number of projects with some of the top names in AI including Google, DeepScribe, Amazon and others. There are AI projects around improving clinical diagnoses, population health, optimizing inpatient care, increasing education around health policy and equity, and using voice-enabled applications. He is excited, in particular, about a couple of projects that use AI to create risk assessments for primary care patients to prevent hospitalizations.  

“We are taking the de-identified version of these records and then building a machine learning algorithm in order to identify patients by risk and see if we can predict their probability of ending up in the emergency room or hospital for the next 12 months,” Lin says. “This is important because the only way we currently know if a patient is at risk for the ED or hospital is if they are in the ED or hospital.” 

The idea behind using AI for risk assessments is to build a prospective predictive algorithm without relying on claims data, which will always be retrospective, Lin says. Using the machine learning algorithm can help primary care physicians be more reactive in helping treat their patient base. Risk prediction, population health management, and clinical decision making are three of the ten areas where primary care doctors could benefit from AI usage, Lin wrote in an article for Journal of General Internal Medicine 

Cancer care 

John Halamka, MD, President, Mayo Clinic Platform, says that when you see the hundreds of places in medicine where you could apply AI, a few areas rise to the top. Considering his wife is a cancer survivor, Halamka is bullish on oncology as one of these leading spots to use AI.  

“There’s clearly a need for being able to predict who will develop cancer to help them avoid cancer. Or once a diagnosis is made, what treatment will be best for their care,” Halamka says. “The increasing use of multi-modal information—phenotype, genotype, exposure information—coming together in AI algorithms, we’re seeing a lot of focus on that. It’s happening both in academia and in the industry.”  

He is excited about the use of AI within radiation oncology as well. He says that typical radiology treatments require a lot of work to prevent nerve or artery damage. At Mayo Clinic, Halamka says the organization has developed a series of algorithms that can reduce the time needed to build the three-dimensional model needed to administer the radiation.