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Amid reports of declining sales and growing skepticism about the utility of machine learning for complex medical research, IBM will reportedly end sales of its Watson AI software used by pharmaceutical firms for new drug discovery.
The health care news web site STAT reported last week that IBM is halting development and sales of its Watson AI drug discovery tools, citing disappointing sales. The company will instead shift the focus of its Watson Health offering to “clinical development,” the web site quoted a source said to be familiar with IBM’s strategy.
Health care industry experts note that AI tools such as Watson may be better suited to applications like diagnostic imaging, where they often outperform humans in terms of objectivity.
The company (NYSE: IBM) did not respond to a request for comment on the report.
In its latest quarterly financial statement, IBM executives sought to reposition Watson Health towards data analytics applications as healthcare clients “look to harness data to create actionable insights.”
Indeed, IBM has been signaling for months that it wants to integrate its Watson AI tools more closely with its expanding cloud platform in advance of what IBM CEO Ginni Rometty calls “the second chapter of cloud” adoption in which enterprises begin shifting core business applications to multi-cloud platforms. Foremost among them would be cloud-based analytics tools used for “harnessing that data, learning from it,” Rometty said.
Among those emerging AI tools is Watson Open Scale, designed to help data scientists track the quality of the machine learning models, including whether its accuracy is high enough or whether bias has been introduced into the model.
IBM’s retreat from costly but potentially lucrative healthcare segments like drug discovery did not come as a surprise to industry insiders.
“Reality is starting to catch up with the hype,” one healthcare executive observed. “After years of non-performance, a cynicism is growing in healthcare about the applicability of AI [and machine learning] to solve clinical diagnostic problems—which is perhaps the biggest problem in healthcare. IBM is mostly responsible for that cynicism, but has infected other firms with similar marketing angles.”
Which is not to say AI and machine learning don’t have a place in clinical settings, often outperforming human clinicians. “Particularly in the area of diagnostic imaging where the machines do a hell of a lot better job not bringing their prejudices, confirmation bias’, to the image,” the healthcare source noted.
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