The Long Road to Health Care’s AI-Based Transformation

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The Long Road to Health Care’s AI-Based Transformation

More than almost any other industry, healthcare is ripe for economic transformation.  Better business models will obviously play a big role, as will low-cost generic drugs.  However, from chronic diseases and cancer to radiology and risk assessment, there are nearly endless opportunities to leverage technology in order to deploy more precise, efficient, and impactful interventions at exactly the right moments in a patient’s care. 

Patients are already demanding more from their providers, while the volume of available data to train AI-systems continues to increase at a staggering rate.  So, in the wake, this one-two-punch, artificial intelligence technology is poised to be the engine that drives cost-effective improvements across the care continuum.

Notably, AI offers a number of advantages over traditional analytics and clinical decision-making techniques.  For instance, learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.

That means, artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces.  It also offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities.  Furthermore, it promises to improve outcomes while dramatically lowering costs.

Unfortunately, the life and death aspect of health care makes its technological transformation much more difficult than that of most industries.  Today, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome barriers to adoption, compliance, and implementation.  This means that despite huge potential pay-offs for consumers and businesses, the pathway to transforming health care via AI may be slow and arduous.

Let’s consider three obstacles to rapid AI adoption, as well as possible ways of handling them, highlighted in a recent issue of Harvard Business Review by health care expert Roger Kuan:

First, developing regulatory frameworks.  Over the past few years, the U...

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