By John P. Desmond, AI Trends Editor
Reports surfaced last week that IBM is contemplating a sale of Watson Health, representing a retreat from the market of AI applied to healthcare that IBM had pursued under the direction of its previous CEO.
The Wall Street Journal last week reported IBM was exploring the sale of Watson Health; IBM did not confirm the report. Ten years ago, when IBM Watson won on the Jeopardy! game show against two of the game’s record winners, the Watson brand in AI was established.
As reported in AI Trends last February, the day after Watson defeated the two human champions on Jeopardy!, IBM announced Watson was heading into the medical field. IBM would take its ability to understand natural language that it showed off on television, and apply it to medicine. The first commercial offerings would be available in 18 to 24 months, the company promised, according to an account in IEEE Spectrum from April 2019.
It was a tough road. IBM was the first company to make a major push to bring AI to medicine. The alarm was sounded by Robert Wachter, chair of the department of medicine at the University of California, San Francisco, and author of the 2015 book The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age (McGraw-Hill). The Watson win on Jeopardy! Gave the IBM AI salesforce a launching pad.
“They came in with marketing first, product second, and got everybody excited,” stated Wachter. “Then the rubber hit the road. This is an incredibly hard set of problems, and IBM, by being first out, has demonstrated that for everyone else.”
Then-IBM CEO Ginni Rometty Used Watson Victory to Launch AI in Healthcare
Ginni Rometty, IBM’s CEO at the time, told an audience of health IT professionals at a 2017 conference that “AI is mainstream, it’s here, and it can change almost everything about health care.” She, like many, saw the potential for AI to help transform the healthcare industry.
Watson had used advances in natural language processing to win at Jeopardy. The Watson team used machine learning on a training dataset of Jeopardy clues and responses. To enter the healthcare market, IBM tried using text recognition on medical records to build its knowledge base. Unstructured data such as doctors’ notes full of jargon and shorthand may account for 80% of a patient’s record. It was challenging.
The effort was to build a diagnostic tool. IBM formed the Watson Health division in 2015. The unit made $4 billion of acquisitions. The search continued for the medical business case to justify the investments. Many projects were launched around decision support using large medical data sets. A focus on oncology to personalize cancer treatment for patients looked promising.
Physicians at the University of Texas MD Anderson Cancer Center in Houston, worked with IBM to create a tool called Oncology Expert Advisor. MD Anderson got the tool to test stage in the leukemia department; it never became a commercial product.
The project did not end well; it was cancelled in 2016. An audit by the University of Texas found the cancer center had spent $62 million on the project. The IEEE Spectrum authors said the project revealed “a fundamental mismatch between the promise of machine learning and the reality of medical care,” something that would be useful to today’s doctors.
IBM made a round of layoffs in the IBM Watson Health unit in 2018, according to another report at the time by IEEE Spectrum in June 2018. Engineers from one of the companies IBM had acquired, Phytel, reported a shrinking client base for its patient analytics solution from 150 to 80 since the acquisition. “Smaller companies are eating us alive,” stated the engineer. “They’re better, faster, cheaper. They’re winning our contracts, taking our customers, doing better at AI.”
Mismatch Seen Between Realities of Healthcare and Promise of AI
This notion of a mismatch between the promise of AI and realities of healthcare was seconded in last week’s Wall Street Journal report that tech companies may lack the deep expertise in how healthcare works in patient settings. “You truly have to understand the clinical workflow in the trenches,” stated Thomas J. Fuchs, Mount Sinai Health System’s dean of artificial intelligence and human health. “You have to understand where you can insert AI and where it can be helpful” without slowing things down in the clinic.
Packaging AI advances in computer science into a viable software product or service has always been a fundamental challenge in the software business. “Watson may be very emblematic of a broader issue at IBM of taking good science and finding a way to make it commercially relevant,” stated Toni Sacconaghi, an analyst at Bernstein Research.
New IBM CEO Arvind Krishna has said AI along with hybrid cloud computing, would be pivotal for IBM going forward. (See AI Trends, November 2020.) Krishna is moving to exit struggling business units and concentrate on those that can deliver consistent growth. As part of this effort, IBM is in the process of spinning its managed IT services division out into a new public company; IT services is seen as a declining margin business by analysts. IBM had $100 billion in sales in 2010 and $73.6 billion last year.
Another challenge for AI in healthcare is the lack of data-collection standards, which makes applying models developed in one healthcare setting and applying it in others is difficult. “The customization problem is severe in healthcare,” stated Andrew Ng, an AI expert and CEO of startup Landing AI, based in Palo Alto, Calif., to The Wall Street Journal.
Healthcare markets where AI has shown promise and achieved results include radiology and pathology, where image recognition techniques can be used to answer specific questions. Also, AI has made inroads in streamlining business processes such as billing and charting, which can help save money and free up staff to focus on more challenging areas. Administrative costs are said to be 30 percent of healthcare costs.
Meanwhile, investment for AI in healthcare continues, with spending projected to grow at an annualized rate of 48% through 2023, according to a recent report from Business Insider. New players include giants such as Google, which has defined a Cloud Healthcare application programming interface (API), that can take data from users’ electronic health records via machine learning, with the aim of helping physicians make more informed clinical decisions. Google is also working with the University of California, Stanford University, and the University of Chicago on an AI system to predict the outcomes of hospital visits
AI is also being applied to the move to personalized healthcare, for example with wearable technology such as FitBits and smartwatches, which can alert users and healthcare professionals to potential health issues and risks.
While retreating from applying Watson in healthcare, IBM is expanding the role of Watson in its cloud service offerings. These include natural language processing, sentiment analysis and virtual assistants, according to entries on the IBM Watson blog,
Read the source articles and information in The Wall Street Journal, in IEEE Spectrum from April 2019, in AI Trends February 2020, in IEEE Spectrum from June 2018, AI Trends, November 2020, from Business Insider and on the IBM Watson blog.