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Challenges for Artificial Intelligence in Medicine

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Re: Challenges for Artificial Intelligence in Medicine

#41

Post by northern_lights »

brandonb wrote: post #37
northern_lights wrote: post #31

Many people might not understand just how busy physicians are, and how difficult it can be to integrate a new product into the clinical workflow. The most pressing thing to understand is that clinicians spend the VAST majority of their time gathering all of the necessary information to make a diagnosis. In other words, they aren't puzzling over how to diagnose about 85% (made that up) of their patients. Once the nece…

One way to think about AI's potential impact is less about replacing what physicians do well currently, and more about doing things they can't do at all. Take ECGs -- it's true that in a hospital, an automated ECG interpretation doesn't buy you much. But what about about the patient with a paroxysmal heart rhythm that doesn't show up when they're at the doctor's office? I was at a patient conference recently, and peo…

To address your example directly - we already have holter monitors that would show a case of atrial fibrillation quite easily. They aren't terribly expensive, at least for something that has to have FDA approval, and they are frequently used. Heck, you don't even need "AI," in the sense of neural networks/machine learning/some other buzzword. Current systems will review a strip collected over several days and flag any abnormal rhythms.

The problem comes with determining who to put on a monitor. In the case of the patients you described, it's actually quite likely that the doctors seeing these patients considered the possibility of afib. The symptoms, though, can be very vague, and they are seen nearly every day in the doctor's office. It's simply too expensive to put every patient on a holter monitor - the doc's office has to be paid to maintain the monitors (which people abuse at home), the nurses have to be paid to teach patients how to correctly wear them, the monitor company has to be paid for whatever absurdly expensive and proprietary review software they supply, and the prescribing doctor (oftentimes the prescribing cardiologist) has to be paid to review and confirm the machine's interpretation.

All of this for a transient rhythm which any second year medical student would easily recognize if presented the EKG from across the room.

The sad reality is that the patients you described were experiencing the system as it is "designed" (I use the term loosely) to work. The fact that someone is persistently seeking help for their problem dramatically raises the probability that something is truly wrong, and doctors actually recognize this and take it into account. This is one of the reasons it's considered best practice to establish a long term relationship with one doctor who knows you well, but it's harder and harder to do with insurance companies only reimbursing for 15 minute visits.

Re: Challenges for Artificial Intelligence in Medicine

#42

Post by dxbydt »

brandonb wrote: post #2

(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 am applying ml to a healthcare domain, and one of the challenges is irrational faith in the outcome. My model says things like - based on your past diet, you must drink more coffee and eat fewer chicken wings and.... When I showed this to a nutritionist, she is alarmed and think people who use my app might switch to an all-coffee diet! Now the ml model is simply interpreting features to minimize the loss function. It doesn't know what is coffee, or what will happen to a human if he switches to an all-coffee diet in reality. One of the VCs said my app was like a GPS for the body. This is awesome and problematic in the same way - if your GPS tells you to turn right and it's pitch dark and you just do what the GPS says and fall off a cliff, is it the GPS's fault ? Perhaps you didn't pay the annual update fee so it's working off of the old maps.

However in the big picture I agree with you. Now is the time to be building these things.

Re: Challenges for Artificial Intelligence in Medicine

#43

Post by mamp »

brandonb wrote: post #2

(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 you are confusing a few issues regarding 'success'. As pointed out Mycin performed well, and since de Dombal's work in the late 60's we knew that computers could perform better than experts in specific clinical domains. Similarly, Internist-1 and other systems performed quite well. The block for them was integration into clinical workflows. The biggest barrier was getting structured data that machines could use to run the algorithms, not the lack of performance.

Today, workflow integration issues still remain, there is still a lot of free text entered etc. However a more pervasive issue is the lack of outcome data against which to train. In other words, what are we optimising algorithms for? In many health care systems we capture raw data, e.g. observations and labs, but not patient outcomes that are meaningful (i.e. based on optimising patient utility vs some more easily captured data).

The final issue for deep nets and ML is that these are descriptive models, they learn from experience, where as we know there is huge variation in practice and outcomes. In medicine we may want normative models based on best evidence, or some combination. And then there's integration with individual patient utilities.

Re: Challenges for Artificial Intelligence in Medicine

#44

Post by randcraw »

brandonb wrote: post #2

(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 work for a large pharma, and the primary uses for AI that I've seen here are 1) biomarker development and 2) a more precise or accurate, and reproducible way to measure medical signals.

Biomarkers predict outcome, of drug effect or of toxins/safety. When used preclinically (non human models) biomarkers continue to be highly valued internally. If a computational model can reliably anticipate outcome, it can shorten trial time and cost. However, in clinical/human use, biomarkers seem increasingly fraught in recent years. However the FDA seems to be less receptive to surrogate outcome predictors, and more demanding of concrete quantifiable clinical adverse events (e.g. the LDL/HDL ratio vs stroke, BMD vs bone breakage, A1C vs retinopathy). As such, I'd be circumspect about developing biomarkers that lead directly to diagnosis. Like the PSA test, even a strong biomarker that also introduces false positives or uncertainty is likely to face opposition to adoption in standard medical practice.

As to the use of AI (esp. pattern recognition) to better measure drug response or toxin effect, this seems to be well received, at least for in-house use. Automation of signal acquisition or analysis, if it can reliably improve on the status quo, in my experience, is well received by my employer. As a drug development cost cutting measure or as a more reliable rater of symptom measurement, AI seems to be win-win. That doesn't mean I see a wholesale rush to adopt AI-related tech here, but the interest seems to be steady and positive. Presumably this should lead to greater use of AI by manufacturers of medical instruments, which frankly I have not seen (though Siemens certainly has hired its share of quants, presumably to serve such ends. However, these folks may well spend most of their time working their magic on external contracts.)

Often in pharma, I think AI, like math models, are perceived by biologists to be too synthetic and abstract, and lack the credibility of a well trod mouse model. Unless AI/quant models lead to a < .05 T Test and a visible separation of error bars between groups, it's unconvincing statistically. And unless the AI can be tied _convincingly_ and directly to an underlying chemical mechanism, it's unconvincing biologically. Scientists are a tough audience.

Re: Challenges for Artificial Intelligence in Medicine

#45

Post by kevinalexbrown »

naveen99 wrote: post #32
kevinalexbrown wrote: post #8

Earlier quoted context omitted.

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…

Ct scans aren't really used to look for brain tumors. We use mri for that mostly. Ct is used for screening of stroke, trauma and other things. Source: radiologist / me. I work on radiology image segmentation also. And I agree it is solvable with machine learning. But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists.

> Ct scans aren't really used to look for brain tumors.

I can see how my phrasing was confusing, but I didn't mean to suggest that Ct scans are used to look for brain tumors. My work segmenting brain scans was not tumor-related, just gray/white matter segmentation.

Re: Challenges for Artificial Intelligence in Medicine

#46

Post by Toenex »

naveen99 wrote: post #32
kevinalexbrown wrote: post #8

Earlier quoted context omitted.

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…

Ct scans aren't really used to look for brain tumors. We use mri for that mostly. Ct is used for screening of stroke, trauma and other things. Source: radiologist / me. I work on radiology image segmentation also. And I agree it is solvable with machine learning. But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists.

> But even if software could do a job of a radiologist, it wouldn't replace one any more than your ekg reading program replaced cardiologists.

I don't think the radiologist is going anywhere soon but the role is changing. Radiologists are increasingly having to deal with more and more derived information. They need to understand the algorithms being used as well as the biology, anatomy, physiology and disease being investigated. I can see a time when algorithmic specialists become a regular part of their multidisciplinary team.

Re: Challenges for Artificial Intelligence in Medicine

#47

Post by YeGoblynQueenne »

brandonb wrote: post #2

(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…

Props for mentioning MYCIN. It's a big bugbear of mine that very few people remember it and you never see it mentioned in articles about AI in medicine.

Re: Challenges for Artificial Intelligence in Medicine

#48

Post by mgreg »

brandonb wrote: post #34
mgreg wrote: post #22

Earlier quoted context omitted.

> 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…

This is an insightful response! I'm curious, what AI application are you working on?

It's, perhaps unsurprisingly, in the diagnostics arena.
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