I'm a bit disappointed in the straw man assumptions in the first paragraph about AI + cats. There's an enormous amount of work being done applying AI and Deep learning to healthcare. Enlitic is one example. The MLHC conference is entirely devoted to the topic. Deepmind's work with the NIH is also well known.
Challenges for Artificial Intelligence in Medicine
21–30 of 48 posts
Re: Challenges for Artificial Intelligence in Medicine
#22(OP here) 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…
> 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?
I'm applying deep learning in a healthcare application and certainly the points you raise resonate and are generally the right areas to address for utilizing AI (for points 2 and 3 really any innovative technology) but let me expand on them a bit.
#2: Deployment and the Outside-In Principle - Very often new technologies are coupled with business model innovation to bring about change. The complex and often perverse mechanics of the healthcare system in this country make this exceedingly difficult. I agree that models that more rationally couple risk with reward (e.g. Accountable Care Organizations, Bundled Payments, and payer/provider organizations like Kaiser) provide the right incentives to reduce cost while increasing the quality of care. This is an environment where technology can make a difference; not so in the fee for service model. I think the software "deployment" model is much less of an issue.
#3: Regulation and Fear - This is actually a significant challenge. The FDA has significant incentive to be very conservative with their approvals (lives may hang in the balance) and for them the risk of failure (i.e. a death caused by a device/test they approved and likely makes the news headlines) is MUCH more traumatic and negative than the rewards of success (some costs are reduced for a specific treatment or diagnostic that almost no one will ever hear about). Additionally the FDA, Doctors, and medical administrators suffer the same fear of formulaic decisions that most people do. We simply don't trust an algorithm to make decisions and so will resist them or hold them to significantly higher standards than we hold human driven decisions. This means even if you get through the FDA you'll potentially have resistance of doctors and patients.
If we export some of the other posts about radiology I can just imagine a patient's experience in a hospital. I'm sure a doctor or radiologist will share an anecdote like the one in this thread (radiologist saw an occlusion due to a poor image and detected a tumor) and scare a patient into thinking a radiologist is better than an algorithm at reading an MRI/x-ray despite statistics that will (eventually) show that the deep learning algorithm will be more consistent and accurate. To be clear I'm not arguing that a radiologist not continue to be involved and review the diagnosis but that it will be difficult for this technology to be established in high-stakes applications due to fear.
Re: Challenges for Artificial Intelligence in Medicine
#23I'm working with a few people on ML applications for medical image segmentation, in Finland and south east Asia. I think ML aided diagnosis will be commonplace pretty soon. 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…
The interesting part is that a lot of effort is already being made to improve those systems. I even know a family doctor who was working in his spare time on improving IT infrastructure.
Could I connect with that family doctor you know? I love speaking with technology-minded individuals in healthcare. My contact info is hello@james.hu
Thanks!
Re: Challenges for Artificial Intelligence in Medicine
#24AI again? Expert systems are around to support medical doctors' decision making for 2+ decades. Studies demonstrated that doctors can use them to improve their decisions. Hardly anybody uses them in practice. In real life, medical information often is stored as PDF or similar in the hospital information system. An interesting challenge for AI would be to encode these PDFs.
Yeah, I build a decision support product that uses an expert system/GOFAI. We parse PDFs, root around the EHR, read and analyze unstructured data, and so on. Parsing pdfs isn't that hard, unless you want to get things like EKG results, then you need to to OCR and and some analysis on the now potentially garbled text.
We have some pretty active users with great results, but doctors are super busy. Its hard to get them to use anything that isn't in their standard tool kit or tie to payments. And, that understandable when you see 14 + patients a day. Getting into the workflow if the real challenge for AI in my view.
Re: Challenges for Artificial Intelligence in Medicine
#25(OP here) 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…
Our startup, CliniCloud, is currently looking at applying deep learning on respiratory recordings obtained from auscultation using our digital connected stethoscope. We've also partnered with teaching hospitals to try and obtained labelled and "clean" data samples to try and use a semi-supervised approach for the detection of asthma and wheeze severity rating.
To be honest, even if we were to stumble across a revolutionary algorithm with 99% sensitivity and specificity, I am sceptical about the dissemination and use of such a product in medicine for at least the next decade.
Re: Challenges for Artificial Intelligence in Medicine
#26(OP here) 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…
I'm not in this field directly, but I have spent a lot of time interacting with scientists and medical professionals regarding new approaches to the field. I'm a little surprised I didn't see more about resistance to unfamiliar technology from the medical field. The essay touched on this briefly, but I find there to be quite a lot of pushback in medicine against new methods in general. In some cases, this is a simple…
What's strange is that although doctors have been reluctant to adopt AI like MYCIN, the pace of adoption for other innovations like new surgeries, drugs, and implantable devices is actually quite rapid. So I don't think doctors reject new methods in general.
I think the hidden factor here is the business model. A new surgery makes money for a hospital, which provides a countervailing force to the caution you mention.
A new diagnosis algorithm may lose the hospital money, so what is the force that is going to push for its adoption?
Re: Challenges for Artificial Intelligence in Medicine
#27I'm working with a few people on ML applications for medical image segmentation, in Finland and south east Asia. I think ML aided diagnosis will be commonplace pretty soon. 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…
Saving time and disparate product integrations are definitely the main requests I see from them. We buy a ton of crap to do patient care and people want widgets that talk with everything. Then someone decides those products are out of date, and so the widgets need to be rewritten. Then there are budget constraints because the hospital's goal isn't to have very quick EMRs running on beefy hardware/infrastructure. But…
"Google should write EMR software. They would probably be pretty good at it."
They (Google DeepMind) are actually doing it for the NHS. I was surprised how much of their work with NHS seems to be UI/UX and "normal" backend/client engineering, rather than ML. It looks very good. I hope they are going to open source it.
Re: Challenges for Artificial Intelligence in Medicine
#28(OP here) 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…
I think there are some folks at UCSB that are working on modeling the health state of trauma patients' based on incomplete and noisy data. Pretty fascinating stuff. I'm mostly familiar with it from talking to Dr. Bernie Daigle at the University of Memphis. I'm curious, since you have a background in fraud detection, are there parallels or insights you brought to healthcare from that area? I'm currently working in fra…
There are lots of parallels to fraud detection! Label imbalance is the obvious one: in both cases, you're looking for the proverbial needle in the haystack, so techniques like anomaly detection or unsupervised learning are really important.
I haven't given much thought to the operations research side of things. I think what I'd ask about any idea there are the same ones I'd ask about any B2B business: first, what's the hard ROI for the customer? How can you measure it? If you succeed, what makes this defensible over time?
Re: Challenges for Artificial Intelligence in Medicine
#29(OP here) 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…
I'd like to apply ML to healthcare, I think it's the holy grail. However, I feel like you're making it much simpler than it really is. You've got a cadre of PhDs at UCSF, how am I going to compete with your all-star team with all of your wealth of knowledge to release something truly innovative?
I read your question a few times but I'm sorry, I still don't understand. Can you rephrase it?
Re: Challenges for Artificial Intelligence in Medicine
#30Earlier quoted context omitted.
I'm not in this field directly, but I have spent a lot of time interacting with scientists and medical professionals regarding new approaches to the field. I'm a little surprised I didn't see more about resistance to unfamiliar technology from the medical field. The essay touched on this briefly, but I find there to be quite a lot of pushback in medicine against new methods in general. In some cases, this is a simple…
What's strange is that although doctors have been reluctant to adopt AI like MYCIN, the pace of adoption for other innovations like new surgeries, drugs, and implantable devices is actually quite rapid. So I don't think doctors reject new methods in general. I think the hidden factor here is the business model. A new surgery makes money for a hospital, which provides a countervailing force to the caution you mention.…
The fee for service model prevalent in healthcare today is certainly one reason. However there is a general mistrust of algorithms. Here's a paper that documents what hey call Algorithm Aversion: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2466040
Note that there are exceptions and this can be overcome.