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IBM Watson Reportedly Recommended Cancer Treatments That Were 'Unsafe and Incorrect'
An anonymous reader quotes a report from Gizmodo: Internal company documents from IBM show that medical experts working with the company's Watson supercomputer found "multiple examples of unsafe and incorrect treatment recommendations" when using the software, according to a report from Stat News. According to Stat, those documents provided strong criticism of the Watson for Oncology system, and stated that the "often inaccurate" suggestions made by the product bring up "serious questions about the process for building content and the underlying technology." One example in the documents is the case of a 65-year-old man diagnosed with lung cancer, who also seemed to have severe bleeding. Watson reportedly suggested the man be administered both chemotherapy and the drug "Bevacizumab." But the drug can lead to "severe or fatal hemorrhage," according to a warning on the medication, and therefore shouldn't be given to people with severe bleeding, as Stat points out. A Memorial Sloan Kettering (MSK) Cancer Center spokesperson told Stat that they believed this recommendation was not given to a real patient, and was just a part of system testing.
According to the report, the documents blame the training provided by IBM engineers and on doctors at MSK, which partnered with IBM in 2012 to train Watson to "think" more like a doctor. The documents state that -- instead of feeding real patient data into the software -- the doctors were reportedly feeding Watson hypothetical patients data, or "synthetic" case data. This would mean it's possible that when other hospitals used the MSK-trained Watson for Oncology, doctors were receiving treatment recommendations guided by MSK doctors' treatment preferences, instead of an AI interpretation of actual patient data. And the results seem to be less than desirable for some doctors.
According to the report, the documents blame the training provided by IBM engineers and on doctors at MSK, which partnered with IBM in 2012 to train Watson to "think" more like a doctor. The documents state that -- instead of feeding real patient data into the software -- the doctors were reportedly feeding Watson hypothetical patients data, or "synthetic" case data. This would mean it's possible that when other hospitals used the MSK-trained Watson for Oncology, doctors were receiving treatment recommendations guided by MSK doctors' treatment preferences, instead of an AI interpretation of actual patient data. And the results seem to be less than desirable for some doctors.
The purpose of such a tool should be to make suggestions that a doctor may not consider themselves. It should be up to the doctor(s) to vet the suggestions or leads before any treatment is actually rendered. A doctor would have to be born in Stupidville to accept bot suggestions as-is.
Table-ized A.I.
How many human doctors did the same or worse?
Mostly random stuff.
In practice the term "AI" is vague and continuous rather than a Boolean designation ("is" versus "is-not"). The term is not worth sweating over. The exception may be if you are making a big purchase and/or investment based on something being "AI". In that case, inspect it carefully rather than assume something with "AI" is smart and/or useful. But that's good advice for any significant purchase: test drive it & ask detailed questions rather than rely on the brochure.
Table-ized A.I.
As such, this machine can find statistical correlations, but it cannot do plausibility checks, because that requires insight. It cannot do predictions either, because that also requires insight.
Neither of these require "insight". They just require more data. With enough examples, statistical correlation is all you need.