Having worked in the field, the model that works well is decision support = augmenting an expert with a computer-aided diagnosis. This can work in a couple of ways, but the two classic ones are:
* Following behind the expert to give a second opinion
* Going ahead of experts to screen cases that should be read by an expert
Humans are imperfect and have their sources of unwanted variability. Algorithms may be less variable and reach near-human performance in controlled settings, but are often not flexible enough, not good at incorporating multi-modal patient history, and sometimes fail in spectacularly bad ways.
Social skills. You still need them, patients still demand them. I am a physician myself and can assure you I think my profession will go the way of the dodo in the next 50 years, starting with surgeons and going on to conservative diagnostics/therapy. The only reason it is not happening immediately is patients still (and for the foreseeable future) demand and expect the physician to be a human as well: showing empathy and bonding the so called "therapeutic alliance" with them.
As soon as AI manages to show that capabilities, it's the end. Algorithms are much more accurate in combining all the available biomedical information needed to decide for diagnosis and therapy.
but doesn't radiology specifically have the reputation for being the "unsung heroes" or at least the unsung among physicians ? with the exception of interventional radiology I thought they hardly ever interacted with pts ?
Indeed. My comment was more general in nature. Specialisations of medicine that have less direct interaction will be the first to be automated. Laboratory ones are pretty much already.
I don't think radiologists are going anywhere, but these tools should at least be used by radiologists. I think one reason they haven't been adopted is for reasons of safety and liability. It would be negligent to run these kind of tools without confirming the results, and if they have to inspect the results anyway, why bother with the computational approach?
I suspect one reason is the schleppiness of implementation. Healthcare is hard enough to navigate as a doctor or patient.
Another is the existence of edge cases. While not quite as bad as self-driving cars, they still require human review.
One radiologist I spoke with was under the impression that his field was going to disappear in the next n years. I don't think that will be the case entirely, but it will change.
One reason why image detection is ripe for this kind of "disruption" is that some of the highest paid medical fields (dermatology, radiology) basically employ expert image recognizers.
On the other hand, I'm not sure what percentage of health care costs goes toward employing doctors directly, but I don't think it's huge compared with the rest of the facility and equipment and administration cost.
The real benefit will come when people don't have to go in to the doctor at all. Which makes the smartphone "take a picture" aspect pretty sweet.
I'm not sure if you're joking or not, but as someone who works for a medical technology company this is very true. Whether or not the use of a particular technology is billable to first order determines whether the technology is adopted.
There may be incentive soon enough, via ACOs and bundled payment programs. When dollars saved go to the bottom line, folks start trying to save dollars.
If we could have a preliminary diagnosis quickly from a machine and only then and if necessary talk to a specialist doctor, the savings in costs could be outstanding. Talk about Affordable Care. Maybe an act of Congress could move this along.
http://suzukilab.uchicago.edu/research.htm
IIRC they were outperforming the average radiologist on some tasks 10 years ago.