IBM Watson Health was committed to building smarter health ecosystems. This means simpler processes, better care, faster breakthroughs, and improved experiences for people around the world. It has the essential capabilities necessary to help its clients drive their digital transformations: deep industry expertise, data and analytics, and actionable insights underpinned by security and trust. IBM spent more than a decade trying to make a go of Watson Health, its moonshot to apply artificial intelligence in healthcare. Watson was supposed to revolutionize everything from diagnosing patients and recommending treatment options to finding candidates for clinical trials.
It was supposed to change health care in a lot of important ways, by providing insight to oncologists about care for cancer patients, delivering insight to pharmaceutical companies about drug development, helping to match patients with clinical trials, and more. It sounded revolutionary, but it never really worked. Recently, Watson Health was, essentially, sold for parts: Francisco Partners, a private equity firm, bought some of Watson's data and analytics products for more than US$1 billion.
The story of IBM Watson is a cautionary tale for any technology that puts marketing before results. IBM led with a utopian vision that they couldn't back up with evidence, technology, or the resources to make it work. And that's a shame because as the most visible clinical AI in the world, it overshadows the real-world impact clinical AI is already having on the world.
Watson Health is not alone in its struggles. Along with IBM, big companies like Google, along with a slew of startups, have stumbled as they have attempted to transform healthcare using AI.
As a machine learning practitioner and health technology entrepreneur who has closely watched these challenges, here are my five takeaways about how to build successful AI products in healthcare and beyond.
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