Challenges for Artificial Intelligence in Medicine

We spend a lot of time thinking about how to make AI succeed in medicine. Given that so many efforts, including MYCIN, have been tried and failed before, one of the key questions to answer is "Why now?" In other words, what has changed in the world which will let AI succeed where it has failed before?

I'm curious: is anybody else here applying deep learning, or any other subfield of AI, to healthcare?

If so... do the challenges listed in this post resonate? Do you believe the shifts identified are the right ones to focus on?

2. Tooling. Spark, Tensorflow, etc.

3. Policy. Folks who make decisions in hospitals are finally coming around to this whole "computer" thing. Slowly, to be sure. Eventually those who don't figure it out will be bought by those who do.

Actually, regulation and fear are the main reasons that machine learning hasn't taken off in clinical medicine. More precisely, the provider's fear of getting sued and the regulations that require a licensed practitioner to "have the final say." There is one more problem as well --> machine learning doesn't solve a problem that providers think they have. It's lesson #1 from The Lean Startup or The Startup Owner's Manual. You may have the best EKG-reading software in the world (I have no doubt computers could surpass providers on this task), but if the providers don't feel they need it, it simply won't be adopted. This is the Watson situation at heart.

Conversely, here are some areas in medicine where machine learning has been adopted:

1. Medical billing code generation: Several companies have systems for reading notes using natural language processing and predicting billing codes using market-basket analysis.

2. Identifying bacterial cultures: Inpatient bacterial cultures are placed in a big incubator and constantly scanned for growth. When growth is suspected, there are emerging algorithms to automatically classify the bacteria. Similar work is being applied to other areas of pathology (see: http://www.nature.com/articles/ncomms12474)

3. Image-analysis in radiology: There are a few radiology companies that are demonstrating superior results by applying novel algorithms. While not "machine learning" per se, the existence of such algorithms is encouraging for future advancements in radiology, since it's a step beyond just viewing the image. Here's one such company that has gained FDA approval for their blood flow mapping technology: http://www.ischemaview.com/

I suspect the first major hospital-facing implementations of machine learning will be in radiology, e.g.: http://suzukilab.uchicago.edu/, which has been diagnosing cancerous tumors in CT scans with neural networks since before it was cool (one reason you won't see the terms 'deep learning' in the literature since they were originally just 3-layer networks, before the term was even coined). IIRC it outperformed the average radiologist.

I wonder if the label problem could be less difficult for some low-hanging fruit. The CT scan neural network required something like 40k labeled scans from a radiologist, but it could come for free: many yes/no disease detections will eventually be resolved by human labeling anyway by your doctor. If you had access, say, to every CT scan taken, and electronic health records for the patients, your labeling is noisy and biased but at least at massive scale. The problem is (legitimately) restricted access to health records in the US. Maybe some European countries have better data access?

And the implementation problem will eventually disappear. I remember talking with a radiologist years ago, who remarked "some people in my field have no idea it's about to disappear". I'm not so sure there will be no more radiologists, but their role will definitely change. Hospitals would be okay with this, actually, since radiologists are expensive. Eventually radiology scans will probably be like ordering blood tests, where fewer and fewer MD's are required.

Here in the UK, DeepMind has been doing interesting work on retinal and radiology images with NHS.

While I agree that large enough quantities of labeled data and legal access to it can be hard to get, interestingly, there are many more low hanging fruit in medtech space that don't necessarily have anything to do with machine learning.

Take hospital IT software for instance. Doctors literally waste double digit percentage of their time wrestling with really bad legacy software.

Even the really expensive solutions, like Epic Systems, is horrible. I am hopeful that better options will become available and future public health budgets don't get wasted on the kind of systems that exists now