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How IBM Watson Overpromised and Underdelivered on AI health Care | 000-151 Real Questions and VCE Practice Test

Editor’s Picks

AI vs. docs

Layoffs at Watson health exhibit IBM’s difficulty With AI

Making clinical AI trustworthy and clear

Conceptual photo-illustration imagining IBM’s AI Watson as a concerned doctor, with the Watson logo standing in for the doctor’s face. Illustration: Eddie man

In 2014, IBM opened swanky new headquarters for its artificial intelligence division, referred to as IBM Watson. inside the glassy tower in reduce long island, IBMers can bring prospective purchasers and traveling journalists into the “immersion room,” which resembles a miniature planetarium. There, within the darkened area, visitors sit down on swiveling stools while fancy graphics flash across the curved screens masking the partitions. It’s the closest that you may get, IBMers every so often say, to being interior Watson’s digital brain.

One stunning 2014 demonstration of Watson’s brainpower showed off its advantage to seriously change medication the usage of AI—a goal that IBM CEO Virginia Rometty frequently calls the enterprise’s moon shot. within the demo, Watson took a bizarre collection of affected person indicators and got here up with a listing of possible diagnoses, every annotated with Watson’s self assurance stage and hyperlinks to helping scientific literature.

in the at ease confines of the dome, Watson under no circumstances did not impress: Its memory banks held competencies of every infrequent ailment, and its processors weren’t vulnerable to the sort of cognitive bias that may throw off doctors. It could crack a tricky case in mere seconds. If Watson may bring that speedy abilities to hospitals and clinics all around the realm, it gave the impression possible that the AI may in the reduction of prognosis error, optimize remedies, and even alleviate doctor shortages—now not through replacing medical doctors but via helping them do their jobs faster and stronger.

assignment: Oncology expert consultant

MD Anderson cancer center partnered with IBM Watson to create an advisory tool for oncologists. The tool used natural-language processing (NLP) to summarize patients’ electronic fitness information, then searched databases to deliver remedy concepts. Physicians tried out a prototype in the leukemia department, however MD Anderson canceled the assignment in 2016—after spending US $62 million on it.

outdoor of company headquarters, besides the fact that children, IBM has discovered that its potent know-how is no healthy for the messy fact of nowadays’s health care equipment. And in attempting to practice Watson to melanoma medicine, considered one of drugs’s biggest challenges, IBM encountered a primary mismatch between the way machines gain knowledge of and the style medical doctors work.

IBM’s daring attempt to revolutionize health care all started in 2011. The day after Watson completely defeated two human champions within the video game of Jeopardy!, IBM introduced a brand new profession route for its AI quiz-show winner: it might turn into an AI medical professional. IBM would take the leap forward expertise it confirmed off on tv—especially, the skill to take into account herbal language—and observe it to medicine. Watson’s first industrial offerings for fitness care would be attainable in 18 to 24 months, the enterprise promised.

in fact, the initiatives that IBM introduced that first day didn't yield industrial items. in the eight years for the reason that, IBM has trumpeted many greater high-profile efforts to boost AI-powered medical technology—a lot of which have fizzled, and just a few of which have failed spectacularly. The enterprise spent billions on acquisitions to bolster its internal efforts, but insiders say the acquired companies haven’t yet contributed lots. And the products which have emerged from IBM’s Watson fitness division are nothing like the amazing AI medical professional that became as soon as estimated: They’re extra like AI assistants that may perform definite events initiatives.

“Reputationally, I feel they’re in some crisis,” says Robert Wachter, chair of the department of medicine on the university of California, San Francisco, and author of the 2015 e-book The Digital medical professional: Hope, Hype, and harm at the dawn of medication’s computer Age (McGraw-Hill). in part, he says, IBM is suffering from its ambition: It became the first company to make a major push to bring AI to the health facility. nevertheless it also earned sick will and skepticism via boasting of Watson’s advantage. “They came in with advertising and marketing first, product second, and received each person excited,” he says. “Then the rubber hit the street. here's an extremely challenging set of complications, and IBM, through being first out, has established that for everybody else.”

in view that 2011, IBM Watson has introduced a large number of tasks in health care. How have they fared? graphic link to table at end of article See table “so far, Few Successes”

At a 2017 convention of health IT specialists, IBM CEO Rometty told the crowd that AI “is actual, it’s mainstream, it’s here, and it can alternate practically everything about health care,” and introduced that it may herald a scientific “golden age.” She’s not on my own in seeing a chance: specialists in desktop science and medication alike agree that AI has the talents to transform the health care business. Yet to date, that advantage has essentially been validated in carefully managed experiments. most effective just a few AI-based mostly tools have been accepted through regulators for use in precise hospitals and docs’ places of work. those pioneering items work frequently within the visual realm, using computing device imaginative and prescient to investigate images like X-rays and retina scans. (IBM doesn't have a product that analyzes scientific photographs, though it has an active research mission in that enviornment.)

looking past images, despite the fact, even nowadays’s best AI struggles to make sense of advanced scientific tips. And encoding a human doctor’s expertise in utility seems to be a extremely complex proposition. IBM has learned these painful training in the market, as the world watched. while the enterprise isn’t giving up on its moon shot, its launch screw ups have shown technologists and physicians alike simply how intricate it is to construct an AI doctor.

The Jeopardy! victory in 2011 confirmed Watson’s magnificent ability with natural-language processing (NLP). To play the game, it had to parse complicated clues filled with wordplay, search large textual databases to locate feasible solutions, and determine the most useful one. Watson wasn’t a glorified search engine; it didn’t simply return documents in line with keywords. as an alternative it employed a whole lot of algorithms to map the “entities” in a sentence and consider the relationships among them. It used this skill to make feel of each the Jeopardy! clue and the tens of millions of textual content sources it mined.

assignment: Cognitive coaching system

The sports clothing enterprise below Armour teamed up with Watson fitness to create a “personal health trainer and tness consultant.” using information from under Armour’s recreation-tracker app, the Cognitive teach turned into intended to deliver customized practicing programs according to a user’s habits, in addition to guidance in response to evaluation of outcomes achieved through similar americans. The educate by no means launched, and under Armour isn't any longer working with IBM Watson.

“It practically seemed that Watson may take into account the which means of language, as opposed to simply recognizing patterns of words,” says Martin Kohn, who become the manager clinical scientist for IBM analysis on the time of the Jeopardy! in shape. “It was an order of magnitude more powerful than what existed.” What’s more, Watson developed this potential by itself, by means of machine learning. The IBM researchers educated Watson by means of giving it hundreds of Jeopardy! clues and responses that have been labeled as appropriate or incorrect. in this complex facts set, the AI discovered patterns and made a mannequin for the way to get from an input (a clue) to an output (a correct response).

lengthy earlier than Watson starred on the Jeopardy! stage, IBM had considered its probabilities for health care. medication, with its reams of affected person information, gave the impression an glaring healthy, in particular as hospitals and doctors have been switching over to digital fitness records. whereas a few of that statistics can also be readily digested by way of machines, such as lab consequences and essential-signal measurements, the majority of it's “unstructured” suggestions, similar to doctor’s notes and sanatorium discharge summaries. That narrative text debts for about 80 percent of a typical affected person’s record—and it’s a stew of jargon, shorthand, and subjective statements.

Kohn, who came to IBM with a medical diploma from Harvard college and an engineering degree from MIT, become excited to support Watson handle the language of medicine. “It gave the look of Watson had the potential to overcome those complexities,” he says. by using turning its mighty NLP expertise to medicine, the idea went, Watson might read sufferers’ health statistics as smartly because the complete corpus of clinical literature: textbooks, peer-reviewed journal articles, lists of permitted medicine, etc. With access to all this information, Watson could turn into a superdoctor, discerning patterns that no human might ever spot.

“doctors go to work day by day—peculiarly the individuals on the entrance lines, the fundamental care medical doctors—with the realizing that they cannot maybe understand every little thing they deserve to understand with the intention to practice the highest quality, best, most effective medicine feasible,” says Herbert Chase, a professor of drugs and biomedical informatics at Columbia university who collaborated with IBM in its first health care efforts. however Watson, he says, could keep up—and if turned right into a tool for “clinical determination assist,” it could allow doctors to sustain, too. In lieu of a Jeopardy! clue, a health care provider might provide Watson a affected person’s case background and ask for a analysis or optimal treatment plan.

Chase worked with IBM researchers on the prototype for a diagnostic device, the element that dazzled guests within the Watson immersion room. however IBM selected now not to commercialize it, and Chase parted ways with IBM in 2014. He’s disappointed with Watson’s sluggish progress in medication on account that then. “I’m now not aware about any marvelous home runs,” he says.

He’s one in every of many early Watson fans who at the moment are dismayed. Eliot Siegel, a professor of radiology and vice chair of assistance systems at the tuition of Maryland, additionally collaborated with IBM on the diagnostic research. whereas he thinks AI-enabled tools should be vital to doctors within a decade, he’s not confident that IBM will build them. “I don’t consider they’re on the cutting edge of AI,” says Siegel. “essentially the most interesting issues are going on at Google, Apple, and Amazon.”

As for Kohn, who left IBM in 2014, he says the business fell into a standard trap: “in basic terms proving that you have powerful know-how isn't adequate,” he says. “prove to me that it will basically do something constructive—that it will make my life more advantageous, and my patients’ lives more suitable.” Kohn says he’s been waiting to look peer-reviewed papers within the clinical journals demonstrating that AI can improve patient effects and keep fitness techniques cash. “up to now there’s very little within the manner of such publications,” he says, “and none of end result for Watson.”

AI’s First Foray Into health Care docs are a conservative bunch—for first rate reason—and gradual to undertake new technologies. but in some areas of fitness care, clinical authorities are starting to see artificially intelligent methods as legit and beneficial. listed below are a few early steps towards AI medicine. Robotic surgical procedure graphic evaluation Genetic evaluation Pathology at present used simplest for routine steps in simple processes like laser eye surgery and hair transplants. consultants are only starting to use automated programs to assist them examine X-rays, retina scans, and other photos. With genome scans fitting a activities part of drugs, AI equipment that without delay draw insights from the information have become crucial. Experimental programs have proved adept at analyzing biopsy samples, but aren’t yet accredited for medical use. clinical-resolution help  virtual Nursing scientific managementMental fitnessHospitals are introducing equipment for functions like predicting septic shock, however they haven’t yet proved their price. Rudimentary systems can check on sufferers between workplace visits and supply automatic indicators to physicians. companies are rushing to present AI-enabled equipment that may enhance efficiency in tasks like billing and coverage claims. Researchers are exploring such functions as monitoring melancholy by way of mining cellular telephone and social media information.

In attempting to convey AI into the sanatorium, IBM become taking up an enormous technical challenge. however having fallen behind tech giants like Google and Apple in many different computing geographical regions, IBM needed whatever large to reside valuable. In 2014, the business invested US $1 billion in its Watson unit, which was establishing tech for varied enterprise sectors. In 2015, IBM announced the formation of a unique Watson fitness division, and through mid-2016 Watson fitness had got 4 fitness-facts organizations for a total cost of about $4 billion. It appeared that IBM had the expertise, the components, and the dedication essential to make AI work in fitness care.

today, IBM’s leaders speak about the Watson fitness effort as “a journey” down a street with many twists and turns. “It’s a tough task to inject AI into health care, and it’s a problem. but we’re doing it,” says John E. Kelly III, IBM senior vp for cognitive options and IBM analysis. Kelly has guided the Watson effort since the Jeopardy! days, and in late 2018 he additionally assumed direct oversight of Watson fitness. He says the company has pivoted when it needs to: “We’re continuing to study, so their choices change as they study.”

task: Sugar.IQ

Medtronic and Watson health begun working together in 2015 on an app for personalised diabetes management. The app works with information from Medtronic’s continuous glucose video display, and helps diabetes patients tune how their medications, food, and culture choices affect their glucose levels. The FDA-authorized app launched in 2018.

The diagnostic tool, as an example, wasn’t brought to market since the company case wasn’t there, says Ajay Royyuru, IBM’s vice president of fitness care and life sciences research. “diagnosis is not the area to go,” he says. “That’s anything the consultants do fairly well. It’s a hard assignment, and no count how smartly you do it with AI, it’s not going to displace the skilled practitioner.” (no longer each person concurs with Royyuru: A 2015 report on diagnostic blunders from the national Academies of Sciences, Engineering, and medicine cited that enhancing diagnoses represents a “moral, expert, and public health necessary.”)

In an try to discover the company case for scientific AI, IBM pursued a dizzying variety of projects targeted to all of the diverse gamers in the health care gadget: physicians, administrative personnel, insurers, and sufferers. What ties all the threads collectively, says Kelly, is an effort to supply “resolution help using AI [that analyzes] massive information sets.” IBM’s most publicized assignment focused on oncology, where it hoped to install Watson’s “cognitive” abilities to turn big information into personalized cancer cures for patients.

in lots of tried functions, Watson’s NLP struggled to make sense of medical text—as have many different AI methods. “We’re doing particularly greater with NLP than they have been five years in the past, yet we’re nevertheless particularly worse than people,” says Yoshua Bengio, a professor of computer science on the tuition of Montreal and a number one AI researcher. In medical text documents, Bengio says, AI programs can’t take into account ambiguity and don’t prefer up on refined clues that a human doctor would note. Bengio says existing NLP technology can aid the health care system: “It doesn’t should have full knowing to do whatever extremely helpful,” he says. however no AI developed thus far can healthy a human doctor’s comprehension and perception. “No, we’re no longer there,” he says.

IBM’s work on cancer serves because the prime example of the challenges the company encountered. “I don’t feel anybody had any conception it could take this lengthy or be this complicated,” says Mark Kris, a lung cancer specialist at Memorial Sloan Kettering melanoma middle, in ny city, who has led his institution’s collaboration with IBM Watson when you consider that 2012.

the hassle to increase cancer care had two main tracks. Kris and other preeminent physicians at Sloan Kettering proficient an AI device that grew to be the product Watson for Oncology in 2015. throughout the country, preeminent physicians at the institution of Texas MD Anderson melanoma core, in Houston, collaborated with IBM to create a different device referred to as Oncology expert marketing consultant. MD Anderson bought so far as trying out the tool within the leukemia branch, but it under no circumstances grew to become a business product.

each efforts have bought strong criticism. One excoriating article about Watson for Oncology alleged that it offered unnecessary and infrequently dangerous innovations (IBM contests these allegations). more broadly, Kris says he has commonly heard the critique that the product isn’t “real AI.” And the MD Anderson task failed dramatically: A 2016 audit via the college of Texas found that the melanoma center spent $sixty two million on the undertaking earlier than canceling it. A deeper analyze these two initiatives reveals a simple mismatch between the promise of desktop studying and the truth of medical care—between “actual AI” and the necessities of a useful product for nowadays’s medical doctors.

Watson for Oncology turned into alleged to be taught by using ingesting the monstrous clinical literature on cancer and the fitness statistics of actual cancer sufferers. The hope become that Watson, with its mighty computing vigor, would examine a whole bunch of variables in these information—including demographics, tumor characteristics, cures, and outcomes—and find patterns invisible to people. it could also keep up so far with the bevy of journal articles about cancer remedies being posted each day. To Sloan Kettering’s oncologists, it sounded like a potential step forward in melanoma care. To IBM, it gave the impression of a good product. “I don’t feel anybody knew what they had been in for,” says Kris.

Watson learned fairly instantly the way to scan articles about medical experiences and examine the simple consequences. but it surely proved unimaginable to train Watson to examine the articles the way a doctor would. “The counsel that physicians extract from an editorial, that they use to trade their care, may no longer be the primary point of the look at,” Kris says. Watson’s thinking is in accordance with statistics, so all it may possibly do is collect records about leading results, explains Kris. “but doctors don’t work that means.”

In 2018, for instance, the FDA accredited a brand new “tissue agnostic” cancer drug it really is effective against all tumors that exhibit a specific genetic mutation. The drug was quickly-tracked in response to dramatic consequences in just 55 sufferers, of whom 4 had lung cancer. “We’re now saying that each patient with lung melanoma should be established for this gene,” Kris says. “all the prior guidelines had been thrown out, in accordance with four sufferers.” but Watson won’t alternate its conclusions in accordance with just four sufferers. To clear up this issue, the Sloan Kettering specialists created “synthetic cases” that Watson could be trained from, almost make-agree with patients with certain demographic profiles and melanoma traits. “I trust in analytics; I agree with it may possibly discover issues,” says Kris. “but when it involves cancer, it basically doesn’t work.”

Do you agree? a few reviews have in comparison Watson for Oncology’s cancer remedy innovations to those of sanatorium oncologists. The concordance percentages point out how regularly Watson’s guidance matched the specialists’ remedy plans. pie chart Sources: Journal of scientific Oncology 2017; melanoma research 2017; JCO clinical melanoma Informatics 2018

The recognition that Watson couldn’t independently extract insights from breaking news in the clinical literature changed into simply the first strike. Researchers additionally discovered that it couldn’t mine tips from sufferers’ electronic fitness information as they’d expected.

At MD Anderson, researchers put Watson to work on leukemia patients’ fitness statistics—and right away found out how hard those information were to work with. sure, Watson had extraordinary NLP knowledge. however in these facts, information could be missing, written down in an ambiguous way, or out of chronological order. In a 2018 paper published within the Oncologist, the crew mentioned that its Watson-powered Oncology professional marketing consultant had variable success in extracting information from text documents in clinical statistics. It had accuracy ratings starting from ninety to ninety six p.c when coping with clear ideas like diagnosis, however scores of most effective sixty three to 65 percent for time-stylish counsel like remedy timelines.

In a ultimate blow to the dream of an AI superdoctor, researchers realized that Watson can’t evaluate a brand new affected person with the universe of melanoma sufferers who've come earlier than to find hidden patterns. both Sloan Kettering and MD Anderson hoped that the AI would mimic the advantage of their professional oncologists, who draw on their experience of sufferers, remedies, and outcomes once they devise a technique for a brand new patient. A computing device that could do the identical classification of population analysis—greater rigorously, and the usage of lots greater patients—would be massively effective.

however the fitness care equipment’s present necessities don’t motivate such true-world getting to know. MD Anderson’s Oncology professional marketing consultant issued most effective “evidence based mostly” innovations linked to official scientific instructions and the outcomes of reports published in the scientific literature. If an AI system had been to base its advice on patterns it found in scientific records—as an example, that a certain class of affected person does better on a definite drug—its techniques wouldn’t be considered proof primarily based, the gold normal in medication. with out the strict controls of a scientific examine, one of these finding can be regarded most effective correlation, no longer causation.

Kohn, formerly of IBM, and a lot of others believe the standards of health care have to change to ensure that AI to realize its full talents and transform drugs. “The gold normal isn't really gold,” Kohn says. AI systems might believe many greater factors than will ever be represented in a clinical trial, and could style sufferers into many greater classes to deliver “really personalised care,” Kohn says. Infrastructure ought to trade too: health care associations should conform to share their proprietary and privacy-managed statistics so AI systems can be trained from hundreds of thousands of sufferers followed over decades.

in keeping with anecdotal stories, IBM has had main issue discovering buyers for its Watson oncology product in the u.s.. Some oncologists say they trust their personal judgment and don’t need Watson telling them what to do. Others say it suggests simplest average treatments that they’re well aware of. however Kris says some physicians are discovering it beneficial as an rapid 2d opinion that they can share with worried patients. “As imperfect as it is, and restrained because it is, it’s very effective,” Kris says. IBM revenue reps have had more good fortune outside the USA, with hospitals in India, South Korea, Thailand, and beyond adopting the expertise. a lot of these hospitals proudly use the IBM Watson manufacturer in their advertising and marketing, telling sufferers that they’ll be getting AI-powered melanoma care.

Conceptual photo-illustration imagining IBM’s AI Watson as a concerned doctor, with the Watson logo standing in for the doctor’s face. Illustration: Eddie guy

during the past few years, these hospitals have begun publishing experiences about their experiences with Watson for Oncology. In India, physicians at the Manipal comprehensive melanoma center evaluated Watson on 638 breast cancer situations and located a 73 percent concordance expense in remedy suggestions; its rating was introduced down by negative efficiency on metastatic breast cancer. Watson fared worse at Gachon tuition Gil clinical middle, in South Korea, where its right options for 656 colon cancer patients matched those of the specialists most effective 49 percent of the time. doctors mentioned that Watson did poorly with older sufferers, didn’t imply certain ordinary medicine, and had a worm that brought about it to recommend surveillance instead of aggressive medication for definite patients with metastatic melanoma.

These experiences aimed to assess even if Watson for Oncology’s expertise performs as anticipated. however no study has yet shown that it benefits sufferers. Wachter of usa says that’s a becoming issue for the company: “IBM knew that the win on Jeopardy! and the partnership with Memorial Sloan Kettering would get them within the door. but they obligatory to show, pretty promptly, an have an impact on on tough effects.” Wachter says IBM ought to convince hospitals that the gadget is value the monetary funding. “It’s in reality vital that they come out with successes,” he says. “Success is a piece of writing in the New England Journal of medicine showing that after they used Watson, patients did more advantageous or they saved money.” Wachter is still waiting to peer such articles appear.

Sloan Kettering’s Kris isn’t discouraged; he says the know-how will handiest get more desirable. “As a device, Watson has fantastic abilities,” he says. “I do hope that the americans who've the brainpower and desktop vigor keep on with it. It’s a long haul, however it’s worth it.”

Some success reviews are emerging from Watson fitness—in certain slender and managed applications, Watson appears to be adding value. Take, as an example, the Watson for Genomics product, which was developed in partnership with the university of North Carolina, Yale college, and other institutions. The device is used with the aid of genetics labs that generate experiences for practicing oncologists: Watson takes within the file that lists a patient’s genetic mutations, and in precisely a couple of minutes it could actually generate a document that describes all of the critical medication and medical trials. “We enable the labs to scale,” says Vanessa Michelini, an IBM distinguished Engineer who led the construction and 2016 launch of the product.

Watson has a comparatively effortless time with genetic guidance, which is presented in structured data and has no ambiguity—either a mutation is there, or it’s no longer. The device doesn’t make use of NLP to mine clinical records, in its place using it handiest to look textbooks, journal articles, drug approvals, and clinical trial announcements, the place it appears for extremely specific statements.

IBM’s partners at the university of North Carolina published the primary paper in regards to the effectiveness of Watson for Genomics in 2017. For 32 p.c of melanoma patients enrolled in that look at, Watson noticed probably crucial mutations now not identified by means of a human evaluate, which made these patients decent candidates for a brand new drug or a simply-opened medical trial. but there’s no indication, as of yet, that Watson for Genomics results in more suitable effects.

The U.S. department of Veterans Affairs makes use of Watson for Genomics studies in additional than 70 hospitals nationwide, says Michael Kelley, the VA’s countrywide application director for oncology. The VA first tried the system on lung cancer and now uses it for all strong tumors. “I do suppose it improves affected person care,” Kelley says. When VA oncologists are opting for a remedy plan, “it's a supply of advice they can deliver to the discussion,” he says. however Kelley says he doesn’t suppose of Watson as a robotic doctor. “I tend to feel of it as a robotic who is a master clinical librarian.”

Most doctors would likely be delighted to have an AI librarian at their beck and speak to—and if that’s what IBM had initially promised them, they may no longer be so disappointed nowadays. The Watson health story is a cautionary tale of hubris and hype. every person likes ambition, all and sundry likes moon pictures, but nobody wants to climb right into a rocket that doesn’t work.

to date, Few Successes IBM started its effort to bring Watson into the fitness care industry in 2011. considering that then, the company has made essentially 50 announcements about partnerships that have been intended to improve new AI-enabled equipment for drugs. Some collaborations worked on equipment for medical doctors and associations; some worked on buyer apps. while lots of these alliances have not yet ended in business items, IBM says the analysis efforts had been beneficial, and that many relationships are ongoing. right here’s a representative pattern of initiatives.

this article seems in the April 2019 print problem as “IBM Watson, Heal Thyself.”


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