AI News, IBM artificial intelligence
How IBM Watson Overpromised and Underdelivered on AI HealthCare
In 2014, IBM opened swanky new headquarters for its artificial intelligence division, known as IBM Watson.
In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of possible diagnoses, each annotated with Watson’s confidence level and links to supporting medical literature.
Within the comfortable confines of the dome, Watson never failed to impress: Its memory banks held knowledge of every rare disease, and its processors weren’t susceptible to the kind of cognitive bias that can throw off doctors.
If Watson could bring that instant expertise to hospitals and clinics all around the world, it seemed possible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages—not by replacing doctors but by helping them do their jobs faster and better.
And in trying to apply Watson to cancer treatment, one of medicine’s biggest challenges, IBM encountered a fundamental mismatch between the way machines learn and the way doctors work.
The day after Watson thoroughly defeated two human champions in the game of Jeopardy!, IBM announced a new career path for its AI quiz-show winner: It would become an AI doctor.
This is an incredibly hard set of problems, and IBM, by being first out, has demonstrated that for everyone else.” At a 2017 conference of health IT professionals, IBM CEO Rometty told the crowd that AI “is real, it’s mainstream, it’s here, and it can change almost everything about health care,” and added that it could usher in a medical “golden age.” She’s not alone in seeing an opportunity: Experts in computer science and medicine alike agree that AI has the potential to transform the health care industry.
(IBM does not have a product that analyzes medical images, though it has an active research project in that area.) Looking beyond images, however, even today’s best AI struggles to make sense of complex medical information.
To play the game, it had to parse complicated clues full of wordplay, search massive textual databases to find possible answers, and determine the best one.
The sportswear company Under Armour teamed up with Watson Health to create a “personal health trainer and tness consultant.” Using data from Under Armour’s activity-tracker app, the Cognitive Coach was intended to provide customized training programs based on a user’s habits, as well as advice based on analysis of outcomes achieved by similar people.
While some of that data can be easily digested by machines, such as lab results and vital-sign measurements, the bulk of it is “unstructured” information, such as doctor’s notes and hospital discharge summaries.
By turning its mighty NLP abilities to medicine, the theory went, Watson could read patients’ health records as well as the entire corpus of medical literature: textbooks, peer-reviewed journal articles, lists of approved drugs, and so on.
“Doctors go to work every day—especially the people on the front lines, the primary care doctors—with the understanding that they cannot possibly know everything they need to know in order to practice the best, most efficient, most effective medicine possible,” says Herbert Chase, a professor of medicine and biomedical informatics at Columbia University who collaborated with IBM in its first health care efforts.
“Prove to me that it will actually do something useful—that it will make my life better, and my patients’ lives better.” Kohn says he’s been waiting to see peer-reviewed papers in the medical journals demonstrating that AI can improve patient outcomes and save health systems money.
“To date there’s very little in the way of such publications,” he says, “and none of consequence for Watson.” In trying to bring AI into the clinic, IBM was taking on an enormous technical challenge.
The app works with data from Medtronic’s continuous glucose monitor, and helps diabetes patients track how their medications, food, and lifestyle choices affect their glucose levels.
The diagnostic tool, for example, wasn’t brought to market because the business case wasn’t there, says Ajay Royyuru, IBM’s vice president of health care and life sciences research.
It’s a hard task, and no matter how well you do it with AI, it’s not going to displace the expert practitioner.” (Not everyone agrees with Royyuru: A 2015 report on diagnostic errors from the National Academies of Sciences, Engineering, and Medicine stated that improving diagnoses represents a “moral, professional, and public health imperative.”) In an attempt to find the business case for medical AI, IBM pursued a dizzying number of projects targeted to all the different players in the health care system: physicians, administrative staff, insurers, and patients.
What ties all the threads together, says Kelly, is an effort to provide “decision support using AI [that analyzes] massive data sets.” IBM’s most publicized project focused on oncology, where it hoped to deploy Watson’s “cognitive” abilities to turn big data into personalized cancer treatments for patients.
More broadly, Kris says he has often heard the critique that the product isn’t “real AI.” And the MD Anderson project failed dramatically: A 2016 audit by the University of Texas found that the cancer center spent $62 million on the project before canceling it.
A deeper look at these two projects reveals a fundamental mismatch between the promise of machine learning and the reality of medical care—between “real AI” and the requirements of a functional product for today’s doctors.
The hope was that Watson, with its mighty computing power, would examine hundreds of variables in these records—including demographics, tumor characteristics, treatments, and outcomes—and discover patterns invisible to humans.
“But doctors don’t work that way.” In 2018, for example, the FDA approved a new “tissue agnostic” cancer drug that is effective against all tumors that exhibit a specific genetic mutation.
It had accuracy scores ranging from 90 to 96 percent when dealing with clear concepts like diagnosis, but scores of only 63 to 65 percent for time-dependent information like therapy timelines.
In a final blow to the dream of an AI superdoctor, researchers realized that Watson can’t compare a new patient with the universe of cancer patients who have come before to discover hidden patterns.
If an AI system were to base its advice on patterns it discovered in medical records—for example, that a certain type of patient does better on a certain drug—its recommendations wouldn’t be considered evidence based, the gold standard in medicine.
Doctors reported that Watson did poorly with older patients, didn’t suggest certain standard drugs, and had a bug that caused it to recommend surveillance instead of aggressive treatment for certain patients with metastatic cancer.
The tool is used by genetics labs that generate reports for practicing oncologists: Watson takes in the file that lists a patient’s genetic mutations, and in just a few minutes it can generate a report that describes all the relevant drugs and clinical trials.
The tool doesn’t employ NLP to mine medical records, instead using it only to search textbooks, journal articles, drug approvals, and clinical trial announcements, where it looks for very specific statements.
For 32percent of cancer patients enrolled in that study, Watson spotted potentially important mutations not identified by a human review, which made these patients good candidates for a new drug or a just-opened clinical trial.
“I tend to think of it as a robot who is a master medical librarian.” Most doctors would probably be delighted to have an AI librarian at their beck and call—and if that’s what IBM had originally promised them, they might not be so disappointed today.The Watson Health story is a cautionary tale of hubris and hype.
AI and Big Data on IBM Power Systems Servers
As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions.
Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations.
In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake.
York Media Relations
Toronto, ON – York University and IBM (NYSE:IBM) have launched an innovative student advising solution that uses Artificial Intelligence (AI) to provide students with advisory services designed to improve their university experiences by delivering both academic and personal guidance covering a wide range of topics in real-time.
Developed collaboratively by York and IBM, this virtual assistant demonstrates how technology – specifically AI – can be used in an educational setting to enhance the quality of the overall student experience.
They are helping the virtual assistant to get better and better at guiding students to the right self-service or in-person contact for academic support or counselling in such areas as mental health, campus involvement, and career services.
“This is a transformative time for learning and York is proud to be collaborating with a global tech industry leader like IBM, to connect our students immediately to the right network of people and supports to help them meet their goals,” said Lisa Philipps, Provost and VP-Academic, York University.
“Together, with IBM's powerful AI technology and York's innovative student services professionals, we are learning how to combine high quality in-person services with real-time information that is delivered to students' personal devices.
The unique virtual assistant is a breakthrough for 24-7 student support services and York is leading the way.” Leveraging IBM Artificial Intelligence technology, the advising solution relies on augmented intelligence to interact with students, letting them communicate in their own words, in English or French.
York students and graduates push limits, achieve goals and find solutions to the world’s most pressing social challenges, empowered by a strong community that opens minds.
- On Tuesday, April 23, 2019
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