The human-computer combination will be a winning combination in digital health, according to Joi Ito, director for the MIT Lab, who spoke at the Partners HealthCare Connected Health Symposium in Boston this week.
"I think there was an announcement recently that Watson is almost finished with med school," Ito said. "It’s sort of a joke, but sort of true. I can imagine Watson being able to ingest all of the data that traditionally might be given to a student in med school. Now imagine if you had a computer that had all of the knowledge that you needed for med school and if it were available all of the time, maybe there’s an argument to be made that you don’t have to memorize it if it’s available all of the time."
Ito suggested that instead of spending so many years trying to memorize things that a computer can memorize, people could spend those years learning how to make the patient feel more comfortable, read signs and symptoms, and increase the number of people who are able to act as an intermediary between the patient and the medical system. He pointed to an example from a Herb Benson book, The Relaxation Response.
"He is about to go to Tibet to study Tibetan medicine and explains the methodology to the Dalai Lama and the Dalai Lama says 'You know, you're not going to get any results because 80 percent of Tibetan medicine is the relationship between the doctor and the patient and you call this the placebo effect. And you're going to control it out, but that is Tibetan medicine.' I think what is interesting is we are looking, in traditional medicine, for that one molecule that does this magic thing, forgetting the 80 percent or something like that is how you feel."
As technologies start to collect and process more medical data, using machine learning, the services that these technologies could eventually provide will allow doctors to shift the work that they are doing to focusing more on the patient, which Ito argues is an area where humans are instinctively good at operating.
One stipulation, he added, is that using machine learning to create digital health offerings for doctors might lead to cultural and ethical biases in the data.
"The thing we have to remember is artificial intelligence will only be as smart as the data we provide it, but it can be smarter about more data than we can because it can process a lot," Ito said. "But for instance, this isn’t the medical example, but in the smart cities thing, one of the applications is to create models that predict crime. If you ingest all of the criminal arrest records and things from New York City, well you’re going to get a bias against African Americans because of the stop and frisk rules and the way that the policing works. And so you're just going to reinforce systemic bias if you just ingest biased data."