How far is artificial intelligence from carrying out a medical diagnosis? AI is a buzzword in the HealthIT field, but its potential has yet to be fully realized. According to a study published in June, “mobile devices can potentially extend the reach of dermatologists outside of the clinic”. Much of skin cancer is diagnosed visually, and researchers attempted to determine the ability of a cell phone camera to detect and diagnose symptoms through a dermoscopic analysis. The researchers noted that many initial results didn’t yield accurate recognition, but after a large sample size had been accumulated there was a rise in the number of correct determinations of skin cancer. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021, and using these mobile devices as instruments to detect carcinomas and other visually prominent health issues can be a huge step forwards in solving a lack of medical practitioners in remote areas of the globe.
Despite the promise that developing technologies are showing, there are surprisingly few practical areas where healthcare is currently being applied. Currently there are too few outlets where both the developing technology and current workflow with patients are in alignment. This has relegated much of the evolution of artificial intelligence to the research realm only. In an article by Evan Sweeney, “data scientists at Booze Allen Hamilton and an emergency physician at Georgetown University School of Medicine highlighted these shortcomings in an article for Health Affairs, highlighting the fact that EHRs don’t have the capability to use machine learning or cognitive computing, and a lack of robust outcome data is holding back third-party applications.
In the short term, the most practical applications for AI may reside in taking on “mundane” data-driven tasks, like reviewing patient charts. To make use of AI’s capabilities in the long term, the healthcare industry needs to find ways to “feed extremely data hungry” with more robust and useful information on clinical outcomes that are not common in EHRs today.
“While we believe machine learning holds great promise, it is far from clear how it will transform health and health care in the short to mid-term,” the researchers wrote. “Today, policy makers and industry executives face decisions about when and how to invest in machine learning to optimize organizational effectiveness and efficiency without wasting capital funds on premature or nonvalue-adding technologies.””
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