Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
ai.googleblog.com
ai.googleblog.com
Google doesn't appear to be claiming that they could replace dermatologists with AI entirely. Lest anyone's mind start leaping in that direction, here are a few more reasons why I think her job is safe for the foreseeable future.
- Much of her work is full-body skin-checks. How able would an AI system be to provide instant feedback on everything sketchy it sees? How comfortable will people be with, rather than a doctor inspecting their entire body, having pictures taken of their entire body?
- Her cases can get rather complicated. Skin problems can be caused or complicated by internal illnesses that need to be considered. This point, in particular, calls on her specialized training beyond what a GP would have.
- There's a lot more to her work than diagnosing. She's also prescribing treatments, monitoring progress, performing surgeries, etc.
- Her time is indeed very expensive (much to by benefit), and more so than a GP's. But there are a lot of challenges involved in deploying things in medicine, and our regulations are probably unprepared for diagnosis-by-computer. I expect it'll be quite a while before, say, a GP augmented by an AI is price-competitive with a board-certified dermatologist.
Geoffrey Hinton personally saying "we should stop training radiologists" just reflects a complete lack of understanding about what a radiologist does.
Bear in mind that there have been some effective ML approaches in radiology (for certain tasks) for decades now and the radiologists themselves have been among the largest hurdles to getting them into clinical use. How they respond to broader and more powerful incursions into "their" space will probably have a significant effect on what their future role(s) and scope are.
I guess what I'm saying is that we can predict a big growth in the consumption of imaging (and other signal) data by clinicians in the next decades, but it is far less clear if the percentage of that mediate through radiology will remain anywhere near as high as it is now.
Recent improvements in algorithmic performance shouldn't blind people to the fact that for decades now ML approaches have been able to beat average specialists for at least some specific tasks, but uptake of CAD systems and the like has been slowed more by culture than V&V of these approaches.
The real value for ML in the clinic is to create or refine signals for physicians from which they might better make a diagnosis. Instead of a model rolling dice and saying "I think this is a malignant nevi", it'd be far more useful for it to say something like "here are 10 other nevi in your hospital which I think are similar and share so-and-such characteristics that ultimately resulted in negative outcomes" or "here's a distribution of likely outcomes I foresee occurring, here are the aspects of the lesions that lead me to believe so".
ML in the clinic (and in most domains imho) ought to be a tool to increase consistency and efficiency of practitioners, not replace them.
Certain centers do use whole body imaging/photography, e.g. this thing: https://www.canfieldsci.com/imaging-systems/vectra-wb360-ima...
I expect it'll be quite a while before, say, a GP augmented by an AI is price-competitive with a board-certified dermatologist.
I'm curious about whether you could just send a (de-identified) photo with maybe some additional basic info to a web service that then spit back a differential. I don't know about costs (sounds relatively low cost to run, maybe a subscription like UpToDate?), reimbursement, or whether this suddenly would be a regulated device, but if I were a GP I would love something like that to help me -- it would make me faster and better at my job. I don't need to compare to board-certified dermatologists.
https://www.researchgate.net/publication/269051304_3D_Skin_T...
Didn't keep up with the clinical research that followed though.
There are enough people in the world which do not get diagnosed at all (third world, poor Americans, etc.).
Your SO makes mistakes as every doctor does.
If the time of your SO is expensive then it means there is a high demand or and high skill required. So we already don't have enough where your SO works.
Imagine your SO being able to save, help even more people.
This type of large scale medical optimization will help so many people around the world, let's hope together Google can make leaps quickly.
https://old.reddit.com/r/eczema/comments/6cci0h/infrared_sau...
I believe the "cold shower" mentioned later is necessary for any effect; my friends usually spend 1-2 minutes immersed in cold water after a heat cycle (arguably both improved blood circulation and immunity boost).
Also, you're basing "most knowledgeable" on how they did in 1 test a decade ago in medical school? I'd say a general physician (and also many specialist physicians) is usually more knowledgeable since they learn about all body systems and deal with them all daily. This is what I notice anecdotally, but I don't have evidence to back it up. I also think it doesn't really matter.
I anticipate three other complimentary technologies which will help many, many people. (In case anyone is looking for medical startup ideas...)
More sensor data from non visible light. Like the recent popularity of using ultraviolet light to see cumulative skin damage.
Better ways to register and align (correct) photos, to better compare images taken over time, to better track progress of conditions over time. Eventually even full body scans.
Some way to measure the thickness or depth of the various layers of skin. Think side scanning radar, but for skin.
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Source: I've had graft versus host disease greatly affecting my skin for nearly 30 years. It's been wicked hard to diagnose and monitor.
That said, it couldn’t be diagnosed by sight alone. It looked very similar to many other skin diseases, and mine in particular comes in many different forms. I’ll obviously be following this research!
FWIW, I got a lot of mileage out of Dr Terry Wahls' thesis about proper, adequate nutrition for mitigating autoimmune diseases. https://en.wikipedia.org/wiki/Terry_Wahls
Also, scanning articles about granuloma annulare, I see light therapy is sometimes helpful. I've done both PUVA and UVB for my condition (GVHD). Both helped, differently, and may give you some relief, or at least get you over the hump. Alas, both are hard to sustain.
As a lifelong patient who has been screwed multiple times by dermatologists, I strongly encourage everyone to
1) Never accept "no" or "I don't know" as an answer. Some one out there has answers for you. Keep searching.
2) Progress marches on and eventually help will arrive. Set up google news alerts for your health conditions (and misc possible treatment technologies). Stay current on the options. Because your doctor's won't be providing that service for you.
I think in dermatology, radiology, and pathology (and other visual diagnostic fields) this work is a huge boon, but the conceptual framework is the same for diagnostics based on laboratory, physical exam, genetic, or any other findings. I don't think ML is necessary in creating excellent decision support tools but is increasingly valuable as you include more and more disparate sources of information.