IBM Tries To Sell Watson Health Again (axios.com) 17
IBM has resurrected its sale process for IBM Watson Health, with hopes of fetching more than $1 billion, people familiar with the situation told Axios. From the report: Big Blue wants out of health care, after spending billions to stake its claim, just as rival Oracle is moving big into the sector via its $28 billion bet for Cerner. IBM spent more than $4 billion to build Watson Health via a series of acquisitions. The business now includes health care data and analytics business Truven Health Analytics, population health company Phytel, and medical imaging business Merge Healthcare. IBM first explored a sale of the division in early 2021, with Morgan Stanley leading the process. WSJ reported at the time that the unit was generating roughly $1 billion in annual revenue, but was unprofitable. Sources say it continues to lose money.
Will they not cherry pick data. (Score:5, Informative)
I had worked with them in the past, their normal response is oh we don't need that data or that other data. Which caused the solution to bomb and for us to stop bothering with implementation because they couldn't make good connections and give good outcomes.
I had asked them if they wanted the prices for the procedures, and they were like no why would we need that? Then they would suggest a 1 time expensive surgery costing $40k vs say 6 months of weekly PT, costing $7200 because without the cost information that they said was useless, the AI will find a different shortest path to success.
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Beyond that, how often did it produce a suggestion that wasn't obvious to the existing medical professionals? Even if it did factor in the stuff that seems currently ignored, would it still produce value above and beyond the healthcare professional norm?
I can easily see how AI could be applied to say, medical imaging where we make compromises for the sake of realistic human review and AI enables a more thorough examination of higher volume of imagery that might have otherwise been skipped as being too impr
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Knowing that a lot of automation is sold on driving down costs rather than improving outcomes, it's more likely that it would empower non-doctors to get a head start on doing all the needed diagnostic tests without pausing to wait on a doctor to review everything. Say the existing data indicates something serious but they would need a blood test and more imaging ASAP, a hospital would rather rely on AI than to have enough competent doctors on staff to order those tests quickly enough.
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Going to my second point, that IBM may be striving for that 'price like a doctor' but the promise of value is explicitly price cheaper than a doctor.
Of course, I could see another hurdle that if the current standard of care means 'doctor' any attempt to optimize out 'doctor' for cost could be a perilous legal situation. Hence the biggest opportunity would be the realm of catching medical situations that would normally have gone undetected where the respective diagnosis would have been skipped due to no reas
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Sounds like these people have absolutely no clue what they are doing. Very smart Dunning-Kruger far left-side cases, obviously.
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I don't see them having a Dunning-Kruger effect, but more to the issue that when they interviewed doctors to get information, there is a lot of stuff that Doctors intuitively know that they don't think to explain it in a way that a bunch of techs would follow the idea.
IBM's rot is rooted in hubris (Score:1)
Garbage in, Garbage Out. (Score:3, Interesting)
The real problem with using Watson to diagnose is Hypochondriacs. People that think they are more sick than they are.
A human doctor gets a feel for if a human is exaggerating, and discounts such activity. A machine will not do this, resulting in far more testing and other such problems.
Of course, the downside of this is that human doctors tend to discount real symptoms, particularly among woman, the poor and minorities. People get less pain medication and sometimes get misdiagnosed because of this.
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Watson never understood or got health (Score:5, Interesting)
I am not surprised at all.
Background: I have spent my whole career in the applications of AI/ML/Data Science to healthcare and the life sciences. In diagnostics, devices, drug development.
Disclaimer, some of this going to be repetitive from my own comment history, such as from back in June when there was a story about Apple struggling in Healthcare:
https://slashdot.org/comments.... [slashdot.org]
The folks at Watson never really understood healthcare. They figured hey we did a great job with Jeopardy, that was AI, now we can do AI in healthcare. But first of all, Jeopardy was a big NLP knowledge graph. Sure that is AI, but the AI for healthcare applications is much more. And even then, you can not just throw âoeAIâ at healthcare, you need to understand all the different subproblems underneath.
Are you talking about drug discovery, medical claims and costs, diagnostics, treatment, devices, population health, genetics, providers, hospital networks, insurers, pharma, etc? What type of data are you dealing with: genetic, omics, imaging (digital pathology, radiology, mass spec, etc), RWE/claims, devices/streaming, behavioral, population health, chemical/molecular/drug entity, etc? And much more. Each has its own nuances, issues, problems, algorithms, approaches.
Slashdot user tomhath previously said it was a
solution looking for a problem, pretty good at parsing English
but not more than that. User technomom has said
It is a brand, nothing more. The original code itself was purpose written specifically to play Jeopardy. There was nothing in it appropriate for medical or other applications.
https://slashdot.org/comments.... [slashdot.org]
https://slashdot.org/comments.... [slashdot.org]
Over the course of my career, I have had multiple different interactions with the folks at Watson Health. It is a huge org, I am sure some people there got it, but I never met anyone who really did. I visited the fancy showcase/partnership development center they have in Boston with all the fancy digital and AR displays.
Looked AWESOME. Lots of money sunk in to impress the hell out of how shiny and cool everything is. It is IBM after all. But a very Potemkin construct.
Once you actually start talking to the people about what they had, how it was developed, how it was validated, why could it be trusted, what circumstances was it strong/weak in, future roadmap, etc. Anything about the real meat and bones. The conversation collapsed. Non technical talking points were regurgitated (we are IBM, this is cool, we know AI, we are working with MD Andersen). It was obvious that the REAL issues of AI in healthcare were not dealt with, it was all quick and dirty pilots, proof of concepts, things slapped together.
At conferences and various industry fora (in the pre-Covid times) I would meet lots of people working in Watson Health. They either did not last long there, or very flashy visionaries talking about broad generalities. There did not seem to be anyone there who could actually get stuff done on the ground. If they were, they were very frustrated I am sure. IBM threw money, charlatans who make careers off big ideas worked there for a bit, but in reality nothing bore fruit. Where were all the real doers?
And you know what, healthcare is HARD. Complex. You would never set things up this way from scratch, but there are reasons why things are the way they are. Scaling things up and dealing with humans, messy, sick, biology with all itâ(TM)s various complications and issues. The recent experiences with Covid should just highlight how messy it all is.
People will do one proof of concept or pilot. In one sub sub sub disease, on a small dataset, from a particular s
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Thank you for those insightful comments - this was a joy to read and has the ring of truth. Over the course of my career in healthcare, I see precisely the same thing, from the opposite direction.
"Healthcare is hard" as you said, but like anything else, not really, once you learn and are in the business. Like everything else, it all makes sense and works "naturally" once you learn to do it. The same is true for engineering, architecture, business, military service, professional sports, arts and entertain
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I work in large-scale education and it was the same thing there at IBM Watson Education - If you imagine a subject - lets just take Volcanoes, shall we: ...
Volcano content - pics, videos, textbooks, lectures, stuff - how do you get it and manage it?
Volcano supplementary content - hints, tricks, questions, mutliple-choice answers, free-text answer assessment, volcano dialogue
Volcano curriculum map - links to lvl2 Volcano content
Volcano state-approved-assessment - did they get a C or an A?
So you talk to the r
Pricing (Score:2)
I'm speaking from absolutely no knowledge, so ignore me as necessary.
If I saw an avocado on sale for $10, I would think, "$10 for an avocado? Bugger off."
But... if I saw an avocado on sale for $100, I would think, "I wonder what's up with this avocado? Maybe I need to try it. Must be something special."
So IBM wants to kill people again? (Score:2)
At least that is what Watson would have done last time if it were allowed to actually trat patients. This instance of Artificial Stupidity did a reasonable job last time, except for the not few cases were it would outright have killed the patient with errors no MD would ever make.
IBM is lone gone! (Score:2)