IBM Watson Health’s stumble and possible fall

July 21, 2021 | by

This Editor hadn’t thought about or seen news about IBM Watson Health in over a year…and likely, neither did you. Granted, our minds have been Otherwise Engaged, but for the company that was supposed to dominate AI and health analytics, it’s notable that TTA’s last two articles mentioning Watson Health was 25 April 2019, on a report that its Drug Discovery unit was being cut back as the latest in a series of executive cutbacks and lawsuits (MD Anderson on a failed oncology initiative), and 14 Feb 2020 on 3M’s lawsuit on unauthorized use of their software.

The New York Times in an investigative piece (may be paywalled or require signup for limited access), brings us up to date on what is happening at IBM Watson, and it’s not bright for Watson Health. IBM, like so many other companies, badly underestimated the sheer screaming complexity of health data. Their executives believed they could translate the big win on the “Jeopardy!” game show in 2011, based on brute computing power, into mastery of healthcare data and translation into massive predictive models. The CEO at the time called it their ‘moon shot’. Big thinkers such as Clayton Christensen chimed in. IBM managers sang its praises to all in healthcare who would listen. This Editor, on a gig at a major health plan in NJ that was ‘thinking big’ at the time and used IBM consultants extensively, in 2012 was able to bring in speakers from Watson for an internal meeting.

But we haven’t been on the moon since 1972 (though probes have visited Mars). Since the big push in 2011-12, it’s been one stumble after another. According to the Times:

  • The bar was set much too high with oncology. Watson researchers knew early on in their research at the University of North Carolina School of Medicine that their genetic data was filled with gaps, complexity, and messiness. The experience was similar with Memorial Sloan-Kettering Cancer Center. The products growing out of the UNC and MSKCC research, Watson for Genomics and Watson for Oncology, were discontinued last year. These were in addition to the MD Anderson Cancer Center initiative, Oncology Expert Advisor for treatment recommendations, that was kicked to the curb [TTA 22 Feb 2017] after $62 million spent. At the same time, IBM’s CEO was proudly announcing at HIMSS17 that they were betting the company on multiple new initiatives. 
  • Watson Health, formed in 2015, bought leading data analytics companies and then didn’t know what to do with them. TTA noted in August 2018 that Phytel, Explorys, and Truven Health Analytics were acquired as market leaders with significant books of business–and then shrank after being ‘bluewashed’. HISTalk, in its review of the Times article, noted that along with Merge Healthcare, IBM spent $4 billion for these companies. IBM’s difficulties in crunching real doctor and physical data were well known in 2018 with revealing articles in IEEE Spectrum and Der Spiegel. 

Six years later, Watson Health has been drastically pared back and reportedly is up for sale. Smaller, nimbler companies have taken over cloud computing and data analytics with AI and machine learning solutions that broke problems down into manageable chunks and business niches.

What’s recoverable from Watson? Basic, crunchy AI. Watson does natural language processing very well, as well as or better than Amazon, Google, and Microsoft. Watson Assistant is used by payers like Anthem to automate customer inquiries. Hardly a moonshot or even clinical decision support. For business, Watson applications automate basic tasks in ‘dishwashing’ areas such as accounting, payments, technology operations, marketing, and customer service. The bottom line is not good for IBM; both areas bring in a reported $1 billion per year but Watson continues to lose money. 

AI as diagnostician in ophthalmology, dermatology. Faster adoption than IBM Watson?

February 3, 2017 | by

Three recent articles from the IEEE (formally the Institute of Electronics and Electrical Engineers) Spectrum journal are significant in pointing to advances in artificial intelligence (AI) for specific medical conditions–and which may go into use faster and more cheaply than the massive machine learning/decision support program represented by IBM Watson Health.

A Chinese team developed CC-Cruiser to diagnose congenital cataracts, which affect children and cause irreversible blindness. The program developed algorithms that used a relatively narrow database of 410 images of congenital cataracts and 476 images of normal eyes. The CC-Cruiser team from Sun Yat-Sen and Xidian Universities developed algorithms to diagnose the existence of cataracts, predict the severity of the disease, and suggest treatment decisions. The program was subjected to five tests, with most of the critical ones over 90 percent accuracy versus doctor consults. There, according to researcher and ophthalmologist Haotian Lin, is the ‘rub’–that even with more information, he cannot project the system going to 100 percent accuracy. The other factor is the human one–face to face interaction. He strongly suggests that the CC-Cruiser system is a tool to complement and confirm doctor judgment, and could be used in non-specialized medical centers to diagnose and refer patients. Ophthalmologists vs. AI: It’s a Tie (Hat tip to former TTA Ireland Editor Toni Bunting)

In the diagnosis of skin cancers, a Stanford University team used GoogleNet Inception v3 to build a deep learning algorithm. This used a huge database of 130,000 lesion images from more than 2000 diseases. Inception was successful in performing on par with 21 board-certified dermatologists in differentiating certain skin lesions, for instance, keratinocyte carcinomas from benign seborrheic keratoses. The major limitations here are the human doctor’s ability to touch and feel the skin, which is key to diagnosis, and adding the context of the patient’s history. Even with this, Inception and similar systems could help to triage patients to a doctor faster. Computer Diagnoses Skin Cancers

Contrasting this with IEEE’s writeup on the slow development of IBM Watson Health’s systems, each having to be individually developed, continually refined, using massive datasets, best summarized in Dr Robert Wachter’s remark, “But in terms of a transformative technology that is changing the world, I don’t think anyone would say Watson is doing that today.” The ‘Watson May See You Someday’ article may be from mid-2015, but it’s only this week that Watson for Oncology has announced its first implementation in a regional medical center based in Jupiter, Florida. Watson for Oncology collaborates with Memorial Sloan-Kettering in NYC (MSK) (and was tested in other major academic centers). Currently it is limited to breast, lung, colorectal, cervical, ovarian and gastric cancers, with nine additional cancer types to be added this year. Mobihealthnews

What may change the world of medicine could be AI systems using smaller, specific datasets, with Watson Health for the big and complex diagnoses needing features like natural-language processing.