And then the machines came for the doctors
digbysblog.blogspot.com
digbysblog.blogspot.com
It might, at the margins, make ‘miracle-worker’ doctors less so. Those doctors will be less able to demand exorbitant fees.
It will also make more-accurate cancer diagnosis available to more doctors, and therefore to more patients. But no one (in the medium term) will not use an oncologist because Watson exists.
These are all good things. In finance, the computers are doing most of the work but employment hasn’t waned (at least not because of the computers).
The underlying reason is that demand for improved results, in both fields, has no foreseeable upper bound. Demand grows to consume the new capacity. There is no upper limit on ‘better cancer diagnosis’, as there is no upper limit on ‘better financial returns’.
Put another way, Watson-assisted diagnosis should be thought of as a ‘new’ product, stoking new demand, in the same way better Macs drive demand every year.
Someday you’ll buy a cheap health robot at Walmart, but this development is not that.
(ps Tyler Cowen’s new book has some interesting stuff on this topic.)
Don't get me wrong, her doctor is a wonderful person that is very knowledgeable, but the way cancer medicine is done today is a lot of guess work and experimentation, with long lead times between measurements (blood tests and whatnot) that are hardly statistically valid and uber prone to tampering (used loosely in the quality control sense).
The existing workforce that will become far more productive are not the doctors. It's the nurses and physician's assistants. They provide value that is much harder to automate, although the Japanese certainly are trying to make progress in this area with humanoid robots to care for patients.
On top of the things you point out, cancer is all about the genes - the genes in the host as well as the myriad of mutations in the cancer.
Cancer treatment of the future will involve sequencing of the patient's genome and repeated sequencing of samples of the cancer itself. The computer will be needed to dynamically account for mutations and prescribe treatment for each one as it arises. Networks of medical computer systems will be able to consult at internet speeds on specific mutations to get suggestions for treatment that are built upon results of other computer prescriptions whose efficacy is much better recorded, processed, and correlated.
As you say, it will be the second tier of the medical industry that most benefits from this revolution. Any nurse who can follow the instructions given by the computer for gathering information, administering tests, feeding results back to the computer, and providing treatment could become a world class oncologist.
Probably doctors will still play a role in some level of oversight of what the computers are prescribing -- but that will fade as our trust level increases over time. Within ten years, computers will be assisting doctors on every diagnosis. Within twenty, they'll be assisting nurses, mostly replacing the need for doctor time. Within thirty years, you'll be able to perform your own interactions with a medical computer as easily as you Google today -- probably easier since it will interact with you using natural language.
5 years ago, I was constantly being told about robot surgery and how it would make surgery safer by reducing mistakes. Hospitals that made these purchases made grand claims in an effort to attract patients. Unfortunately, the da vinci robot has continuously injured patients[1][2] while the marketing has made it so the machines continue to be installed in more hospitals as patients demand the robot surgeons.
Hospitals that have purchased a da vinci machine are very unlikely to come out and say it was a waste of money and is injuring patients, it makes them look like they are fools, and increases their liability. I really hope Watson is as advanced as described in the Wired article, but there have been plenty of popular science rags that similarly pimped robots as making surgery safer.
I think eventually robots will eventually make surgery safer, and watson-like computing will make better diagnoses, but there clearly was a mistake made in evaluating robots for surgery that I would rather not see made again.
[1] http://www.bloomberg.com/news/2013-10-08/robot-surgery-damag... [2] http://online.wsj.com/news/articles/SB1000142405270230470310...
Doctors might like reading research every once in a while. They don't like spending all their time on it. An overworked doctor should not care if he refers a patient to an overworked oncologist or asks watson first.
Computer vision applied in health care also automates tasks that are mind-numbing. Is it really necessary for highly-skilled doctors with decades of experience to look through a microscope?
Doctors also complain that they don't have enough time for their patients. Watson seems to be excellent at completing paperwork. Given that we hardly have enough doctors anywhere in the world, more AI will be good thing.
Full Disclosure: I just read a paper saying that only 10% of doctors are capable of bayesian reasoning, so I'm in a mood to pick on doctors today. I'll stop now.
This implementation of Watson is more than a query engine; it returns a confidence interval with its diagnoses and "knows" which questions to ask to improve the confidence level in those diagnoses.
1. Doctors on average miss 50% of questions needed to be asked on a medical interview.
2. In general , people are less likely to lie to a machine, when they would lie to their doctors if dealing with some socially sensitive issues.
Don't get me wrong - legal automation is coming but it is only replacing the least skilled. Paralegals, procurement officers and law students have the most to fear.
Perhaps I'm a luddite, but I wonder if this time it really "is different" or if we'll discover new ways to put human labor to work, just as we did when farmers left the land or when services overtook manufacturing as the primary sector creating jobs (in the developed world, at least).
This isn't after-the-singularity type science fiction where we're adding assumption upon assumption to several levels beyond what is possible.
This is applying a technology (that's already proven at performing similar complex deductive analysis over massive quantities of data) to a new discipline. Any assumptions about what we can do in the new discipline with the current technology are minimal and extremely likely overcomable.
Even if Watson isn't quite there, the way is pretty clear now. Linear improvements in the Watson approach will yield results.
On the machine learning side, there is very little in the way of inference. Most of machine learning is an attempt to solve the statistical problem of predicting some value Y (e.g. a diagnosis) given a feature vector X.
To obtain data from medical texts (even with human assistance) and use this to form diagnoses of actual patients, would require solving both these problems. We would have to parse and represent the complex logical relationships that are expressed in academic papers. We would then have to have machine learning methods that can work with complex data (not just feature vectors) and combine inference with statistical learning.
And isn't doctors and insurance companies being impressed with results are another hint that watson has those capabilities(or is a very big fraud, something IBM wouldn't riskm) ?
Unlike with jeopardy, you do not have a huge well organized catalog of information lovingly curated by trivia worshiping nerds. It is a ridicules stumbling block but there you have it. Health records are not normalized and you can't touch them anyway, research is closer to human readable prose and filled with jargon - good luck developing NLP for it if you're "just a programmer". Hell, look at their textbooks and the way they study and are trained, that screams for computerized improvement and is still a tall order.
Watson is able to parse natural language and pull out possibly important information. It then applies some very state-of-the-art machine learning to derive the most likely contextual meaning of that information. That contextual meaning can be cross-referenced with billions of other pieces of weighted information to derive further insights.
I come from a family full of doctors. They tend to be smart people, but as Jeopardy showed, even the best player on earth is no match for a machine trained at that discipline.
Once we start adding in genetic information, that will allow machines like Watson to customize diagnoses and treatments based upon your specific genetic profile.
The way they are implemented is by people entering data in an orderly fashion, both those who built the systems ,and doctors . This somewhat limits the system, both from breadth of research and data entry time at the doctor.
But even with those limitations , those systems offer far better diagnoses , especially in the rare cases,than you average and even great doctor.
For that matter it is hard to coordinate and record treatments if the process of diagnosis is too complicated or arcane for AI to understand.
I would rather stay perfectly healthy, young forever, and not pay a cent. Wouldn't you? If moving towards that goal puts some doctors out of a job, well so be it. The end goal doesn't include their job anyway. Maybe they can go become engineers and create value, or go make better medical machines.
My problem with machines is that sooner or later people cause changes while machines imprint status quo.
You could also expand that idea to having it acting as a spam filter for medical publications. It could scan every old and new published paper for basic validity. This wouldn't prevent fraud, but it might raise the bar for publishing bad science.