IBM's Watson To Be Used For Cancer Treatment 46
Beeftopia (1846720) writes "The New York Genome Center and IBM will investigate whether Watson can be used to parse cancer genome data and then recommend treatments. The trial involves 20 to 25 glioblastoma patients with poor prognoses. The article states, 'It should theoretically be possible to analyze [genomic] data and use it to customize a treatment that targets the specific mutations present in tumor cells. But right now, doing so requires a squad of highly trained geneticists, genomics experts, and clinicians. It's a situation that can't scale to handle the [number of] patients with glioblastoma, much less other cancers. Instead, that gusher of information is going to be pointed at Watson... Watson will figure out which mutations are distinct to the tumor, what protein networks they effect, and which drugs target proteins that are part of those networks. The net result will be a picture of the biochemical landscape inside the tumor cells, along with some suggestions on how clinicians might consider intervening to change the landscape.'"
Neurosurgery Research (Score:5, Insightful)
This is good news for the future of medical AI, but in a frank discussion with a neurosurgeon about research in this area, he admitted that glioblastoma patients are the first place we see a lot of experimental treatments because their prognosis is so poor they'll try anything. If you come up with a mildly reasonable excuse to hit them in the head with a brick, they'll jump at the opportunity to use brickotherapy to cure their cancer.
The point is, the extremely poor prognosis means that there's a low bar for something to work, not that this is the area Watson is most needed or could make the greatest impact.
I still view it as an extremely positive development, just trying to temper the enthusiasm.
Re:Faster, but smarter? (Score:5, Insightful)
Lets say hypothetically, just pulling numbers out of no where, that Watson is 1/10th the price of a specialized team but only half as accurate. I think those numbers are probably a bit on the pessimistic side... yes Watson is an expensive system but each query will be completed in minutes or at most hours, the marginal cost of each additional patient just isn't very high when compared to a multidisciplinary team of geneticists, oncologists, toxicologists, and general practitioners. As for accuracy, well to put it simply this kind of network analysis is what Watson was designed from the ground up to do and it does it shockingly well.
But I digress, back to the example. Lets say there are enough specialized teams to treat 1,000 glioblastoma patients per year and they successfully treat 80% of patients. 800 saved hurray! But, for the same price, Watson could develop treatments for 10,000 patients, saving 4,000 of them. The of course there is the fact that Watson is not a build once and done kind of system. Every year there'd be 10,000 new pieces of information to be entered into the system, refining the probabilities further and further. Sooner or later, Watson will be not just equal to the human team, but will far surpass it.
Re:Faster, but smarter? (Score:5, Insightful)
I wonder if it can come up with more accurate answers over a longer term vs. a team of researchers and scientists.
Well, the point of TFA is that a "team of researchers and scientists" is not actually available or affordable for the vast majority of patients. The possibility that they might be better in theory, doesn't really help with reality. Also, Watson should improve with feedback, more data, and increased computing power.
Re:Why is Watson needed for this? (Score:4, Insightful)
There are a couple of possibilities. It is entirely possible that the very smart people working on Watson recognized some overlap between the two problem sets and recognized that they could apply lessons learned to this new goal. It is also entirely possible that marketing decided to "cure cancer" and paid very little heed to what Watson did in the past.