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Challenges for Artificial Intelligence in Medicine
11–20 of 48 posts
Re: Challenges for Artificial Intelligence in Medicine
#12(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 met with a company recently that started out on the clinical side doing fMRI brain imaging. They currently house the nations largest private dataset and have some very compelling sub-datasets in various areas, such as Parkinsons, ADHD, Alzheimers, and Strokes.
They've hit the point of gaining enough training data and were moving onto the next phase where they wanted to utilize deep learning to help augment the doctors decision making. There's a ton of red tape here with the FDA but that was the near term goal. Augment the doctors decision making but not replace them.
I believe they'll succeed in doing this one way or another. We had talked about also pairing brain imaging data with genetic data (eg. Alzheimers and mutations on APOE). The critical things we talked about were how we actually train the different models, what a sequential approach towards this type of software development would look like, etc. We believed that the most pertinent focus point would need to be a refined supervised deep learning model.
I can definitely sympathize with the complexities of deep learning in healthcare.
Re: Challenges for Artificial Intelligence in Medicine
#13(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…
MYCIN did not fail: https://www.ncbi.nlm.nih.gov/pubmed/480542
From the abstract: "MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%." So, better than the human experts considered individually.
Expert systems are able to explain their reasoning, which is essential if they are to be used for diagnosis. Neural networks cannot.
Deep learning and similar approaches might be useful in two areas though: interpretation of images, e.g. what's on this X-ray or ultrasound scan, or what type of rash is this; and undiscovered associations, e.g. are patients who were given drug X combined with drug Y for disease P more likely to get disease Q later on in life?
Deep learning in medicine also has a downside even supposing it works: lots of patient records are required, and anonymous ones can be linked to they people they describe, so there's a confidentiality problem.
PS I notice that you appear to be agreeing with my in your article: "This was the MYCIN project, and in spite of the excellent research results, it never made its way into clinical practice." and even refer to the same paper, so we're really just using different criteria for success/failure. One of the problems of expert systems was getting them adopted by end users. I don't see how using neural networks will be any different in that regard.
Re: Challenges for Artificial Intelligence in Medicine
#14I'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.
Re: Challenges for Artificial Intelligence in Medicine
#15(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've used deep learning for segmenting brain anatomical scans, and I worked in a lab that used neural networks to detect cancerous tumors. 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…
My Dad is a consultant radiologist, recently retired but still doing some part time work. When I've had conversations about whether he thinks his job might be automated, he says that the main problem he's seen with current ML systems is that they throw up far too many false positives. They also aren't great for unusual or corner cases. For example, in one scan he saw recently the very corner of the picture was occluded because the radiographer had left something on the machine (not quite sure what). He said most trainees would have just ignored it, but he sent it back. Turns out it was hiding a tumour. Now I know this is just one example, but he said you'd be surprised by the number of weird things that turn up like that. Where he does see it having a place is to help radiologists from missing really obvious things because they're tired. Most people don't realise how much concentration is required to just look at scan after scan for miniscule clues indicating a potential problem.
Re: Challenges for Artificial Intelligence in Medicine
#16AI 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.
Re: Challenges for Artificial Intelligence in Medicine
#17(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've used deep learning for segmenting brain anatomical scans, and I worked in a lab that used neural networks to detect cancerous tumors. 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…
Many of the challenges with radiology are historical. Imaging technologies have been treated as a devices for producing images on which measurements can be made, rather than a measurement device from which images are formed. This has lead to difficulties for quantitative imaging techniques and so we continue to rely on the (albeit impressive) qualitative assessment of the radiologist.
This is changing (shameless plug for my company http://www.pulmolux.co.uk) but medicine moves slowly preferring evolution over revolution. Having said that, I certainly detect that the scanner manufacturers (GE, Philips, Siemens, etc) having reached saturation with radiologists now have a thirst for disruption and see the referring physician as the next customer. MRI cardiology being something of an example.
Re: Challenges for Artificial Intelligence in Medicine
#18I'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.
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Re: Challenges for Artificial Intelligence in Medicine
#19(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 was a bit surprised that you seem to consider the challenges to be "technical, political and regulatory". Why? Because obviously one word is missing and that's "science".
Maybe the lack of adoption of machine learning (I don't like to call something AI that really isn't) is due to the fact that medicine is more demanding on high quality scientific evidence than the IT industry.
Related, Gerd Antes from Cochrane once wrote a very interesting piece on the promises of "Big Data" (which is kinda related) and that they still need to hold up to scientific evidence: http://www.labtimes.org/editorial/e_654.lasso
Re: Challenges for Artificial Intelligence in Medicine
#20I'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 naturally if you show them how much time/money is wasted waiting on a slow database call, it gets ignored.
Google should write EMR software. They would probably be pretty good at it.