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Debunking the robodoc: the pivotal role of human-machine collaboration in the future of diagnostics

Debunking the robodoc: the pivotal role of human-machine collaboration in the future of diagnostics

It’s no secret that the future of nearly every industry will involve innovative applications of data. In the healthcare industry, specifically, large-scale, data-driven decision making has the potential to generate as much as $100 billion of value by improving the efficiency of research and clinical trials and building new tools for physicians, consumers, insurers, and regulators that improve and personalize the patient experience.

SwissCognitiveArtificial intelligence () tools are poised to play a significant role in what may ultimately be a transformative era for the industry — specifically those featuring “” capabilities. Research from growth consulting firm Frost & Sullivan suggests that the healthcare market will experience a compound annual growth rate of 40 percent through 2021, at which point it will account for over $6.5 billion of earned revenues. Impressive progress in -based diagnostics

A variety of research confirms that this transformation is indeed already underway. In 2016, researchers from Beth Israel Deaconess Medical Center and Harvard Medical School used a algorithm to assess whether a cluster of lymph node cells contained cancer. Their algorithm achieved a diagnostic success rate of 92 percent, slightly below the 96 percent success rate achieved by human diagnosticians.

In a more experimental vein, Israeli researchers created an -based device designed to recognize — and differentiate among — 17 different disease conditions, including chronic kidney failure, pulmonary arterial hypertension, and lung cancer based only on samples of patients’ breath. Using an artificially intelligent nanoarray built with molecularly modified gold nanoparticles, the team assessed breath samples from 1,404 patients, correctly diagnosing 86 percent of them.

Where we really stand

With studies like these emerging more frequently, it can be challenging to discern what it all really means for the industry — both in the immediate future and far beyond.

Considering that diagnostic errors contribute to roughly 10 percent of patient deaths and between 6 percent and 17 percent of all hospital complications, -driven technologies in the mold of those outlined above have the potential to make substantial contributions to the healthcare industry and the patients it serves. Not only are algorithms quickly approaching human-level diagnostic success rates, they are doing so on timelines of which humans are quite literally incapable. In healthcare — where time is a precious resource — this efficiency is tremendously valuable.

But as promising as many of these explorations of -based diagnostics may be, it’s critical to place them in their proper context. For one, is unlikely to replace your doctor anytime soon. While its diagnostic success rates are undoubtedly impressive — and will only get better with subsequent refinements — they don’t always tell the whole story. In short, contrary to what the headlines may have you believe, when it comes to healthcare diagnostics, statistical accuracy is not the only metric about which we need to be concerned. In fact, this is one of the major reasons why, even as these technologies mature, human diagnosticians needn’t fear being replaced by a . […]

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