The demise of IBM’s Watson Health would be easy to file away as another ill-fated attempt to tackle health care without understanding its complexities. But its downfall offers specific lessons on the implementation of artificial intelligence in an industry still clamoring for new ways to mine mountains of data.
One of IBM’s biggest mistakes, business analysts and informatics experts said, was picking overly ambitious targets — such as cancer treatment and drug discovery — but failing to engage deeply enough in the science to build products that would demonstrably improve outcomes.
Those conclusions are formed with the benefit of hindsight, and even critics of the company’s approach agree it deserves credit for being a pioneer in applying a new generation of AI in medicine. But if IBM was among the first to explore that frontier, its downfall is a case study of mistakes that the next wave of entrants would do well to avoid.
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