The arrival of artificial intelligence and its ilk — cognitive computing, deep machine learning — has felt like a vague distant future state for so long that it’s tempting to think it’s still decades away from practicable implementation at the point of care.
And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note.
Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people.
Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning.
‘Our initial application of deep learning convinced me that these methods have great value to healthcare,’ said Andy Schuetz, a senior data scientist at Sutter Health’s Research Development and Dissemination Group. ‘Development will be driven by our acute need to gain efficiency.’
Schuetz and his colleagues are not alone. By as soon as 2018, some 30 percent of healthcare systems will be running cognitive analytics against patient data and real-world evidence to personalize treatment regiments, according to industry analysts IDC.
What’s more, IDC projects that during the same year physicians will tap cognitive solutions for nearly half of cancer patients and, as a result, will reduce costs and mortality rates by 10 percent.