>AI software that helps doctors diagnose diabetic retinopathy like specialists is approved by FDA (theverge.com)
FDA permits marketing of AI-based device to detect diabetes-related eye problems
51–60 of 104 posts
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#52Ultimately, you will need a doctor somewhere along the diagnostic process so that someone is there to assume liability for incorrect diagnoses.
What's crazy is that if AI is better than doctors by some significant degree, what do we do when the doctor and AI disagree? Like if doctors are right 85% of the time, but the AI is 90%. I guess we treat it as another doctor? Like if we have 4 opinions that agree, we go with that one regardless of the source of those opinions (as long as they meet some minimum competence threshold).
This isn't a new issue, there have been CAD systems that outperformed average clinicians (on very specific tasks) since at least the mid 90s. At the end of the day in some jurisdictions liability drives the resolution process, efficiency in others.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#53Ultimately, you will need a doctor somewhere along the diagnostic process so that someone is there to assume liability for incorrect diagnoses.
What's crazy is that if AI is better than doctors by some significant degree, what do we do when the doctor and AI disagree? Like if doctors are right 85% of the time, but the AI is 90%. I guess we treat it as another doctor? Like if we have 4 opinions that agree, we go with that one regardless of the source of those opinions (as long as they meet some minimum competence threshold).
This is definitely going to be an issue. Even in cases where you're measuring your tool against "expert consensus" (often 3-5 physicians), there's a reasonable likelihood that the consensus may be wrong in certain types of cases.
Though even in those cases, you might be looking to show that your tool agrees with physicians at least as often as physicians agree with each other. Malpractice is usually about failing to offer the standard of care, and if you can show a reasonable level of concurrence with the standard of care in research and trials, you may be able to move forward and reach those higher levels of accuracy.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#54Ultimately, you will need a doctor somewhere along the diagnostic process so that someone is there to assume liability for incorrect diagnoses.
What's crazy is that if AI is better than doctors by some significant degree, what do we do when the doctor and AI disagree? Like if doctors are right 85% of the time, but the AI is 90%. I guess we treat it as another doctor? Like if we have 4 opinions that agree, we go with that one regardless of the source of those opinions (as long as they meet some minimum competence threshold).
Perform more tests. Start preliminary treatment and continue to monitor. Medical care isn't a 1-bit decision process.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#55Earlier quoted context omitted.
Generally, the FDA (or the government body responsible for certifying medical devices) does not conduct code reviews in the sense of looking at the code and trying to find bugs. The way it works is: the manufacturer of a medical device assesses the harm that can be caused by a software malfunction, and assigns it a safety classification (class A, B or C). Class A is used when no injury is possible, and class C is use…
> class C software it requires whitebox tests as well What does this mean?
test the internal components, not just the externally visible performance.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#56I wonder what factors made this decision possible? I love the idea of automated diagnosis but the performance rates are 87% true positive and 90% true negative in the article. Seems a bit low. Maybe people aren't getting diagnosed at very high rates? That would be a reasonable justification for deployment with somewhat less than perfect accuracy. Anyone have any insight?
The status quo is screening at the eye doctor. This enables screening at primary care visits. People mostly go to primary care more often than the eye doctor. The medical risk is that people will forgo other screening for 12 months when given a negative result. The cost of additional screening for false positives is the other big downside (this is all the machine does, recommend a specialist visit or to rescreen in a…
> People mostly go to primary care more often than the eye doctor.
Really? Standard advice is 1/yr for both.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#57Does the FDA conduct code reviews? And how do they guarantee that the code or the training data does not change over time without them knowing?
Generally, the FDA (or the government body responsible for certifying medical devices) does not conduct code reviews in the sense of looking at the code and trying to find bugs. The way it works is: the manufacturer of a medical device assesses the harm that can be caused by a software malfunction, and assigns it a safety classification (class A, B or C). Class A is used when no injury is possible, and class C is use…
Worth noting that this is a rough description of how IEC 62304 looks at the problem, but adherence to that standard is not required by many regulatory bodies (including FDA, although there is guidance). It's a good approach to this, but there are others.
More generally, the regulatory body will be looking for you to have a formal engineering process in place and be able to demonstrate its efficacy. Part of that will be looking for how you do hazard and risk analysis, how you handle CAPA (corrective and preventative actions in FDA-speak), how you do system trace, design history file generation, etc. etc. That you have a software development plan and can demonstrate how you follow it.
So they aren't really interested in code reviews per se, but they are very interested in how you view code reviews, how you perform them when you do, what gets documented, how you perform trace an V&V etc.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#58The company's board (politcos) and syndicate (there is none) is a bit weird for a FDA approval...maybe there are some caveats or scope notes that I have not seen.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#59Does the FDA conduct code reviews? And how do they guarantee that the code or the training data does not change over time without them knowing?
See other comment about code reviews.
In general though approval to market for particular indications is for one fixed configuration of a product, so your model parameters won't change.
All of this is in the process of being hashed out, but I expect for a while at least if you are doing on-line learning it will be in non-clinical configurations only and you will end up releasing an update periodically. Depending on the changes this may need a new 510(k) or not, but would definitely need a formal release.
Re: FDA permits marketing of AI-based device to detect diabetes-related eye problems
#60>> In one clinical trial that used more than 900 images, IDx-DR correctly detected retinopathy about 87 percent of the time, and could correctly identify those who didn’t have the disease about 90 percent of the time.
I read that as .87 accuracy, .9 specificity (True Negative Rate). However, I can't find in the link provided in the article above the sensitivity (recall or True Positive Rate).
I'm guessing it goes a bit like this (assuming perfectly balanced classes which in reality they aren't):
Predicted + Predicted - Total
Actual + 378 72 450
Actual - 45 405 450
-------------------------------------
Total 423 477 900
Accuracy: 0.8700
Error: 0.1300
True Positive Rate: 0.8400
True Negative Rate: 0.9000
Precision: 0.8936
Recall (TPR): 0.8400
F-Score: 0.8660
Off the top of my head, it looks like automated screening will do some good and probably more good than harm, but without knowing how doctors judge good vs harm there's no way to know for sure how useful this device will really be.