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Earlier quoted context omitted.
Seems like they are using this to get a proper referral to a specialist rather than using this as sole diagnosis. The code itself is probably 15 lines using Tensorflow or other framework I'm guessing, but could be wrong.
If it is that simple I am wasting my time in my current job.
Earlier quoted context omitted.
I think the number is closer to 6%. 90% of people are accurately detected as not having the disease, i.e. 10% FP rate. So ~3m people would be falsely diagnosed as having the complication, to 200k/3m have the disease. 10% isn't a great number, but it isn't clear from this coverage whether this complication is generally asymptomatic or not. If there are symptoms to go with it, the numbers may be far better.
Also keep in mind that if there are positives general practitioners would refer to a specialist anyways for treatment. These specialists would be more than equipped to detect false positives. Teleretina imaging is becoming more and more prevalent as well with eyePACS and Welch-Allyn having dedicated interpretation services, so patients wouldn't have to necessarily go somewhere for verification. I'm more worried about…
I think the title is fine, but a lot of the comments are applying what the software does to medicine in general. What this software is used for is very specific, but also very useful in that it is a common medical problem. It is used only to help diagnose diabetic retinopathy (ie eye damage caused by diabetes). This is AI Vision software used to analyze a photograph of someone's retina to detect damage. In essence it…
This is one of the classic AI application domains -- for example this is why Stanford's Knowledge Systems Lab was near to the medical school and why the mainframe Zork was developed was owned by MIT's Medical Decision Making group.
The company is saying you don't need a specialist, but after bayes theorem (using 90%TN 87%TP and D(A)= 200,000 complication / 29,100,000 diabetes), the chance you have this condition after the machine says you do is 0.83%.
What if you have the condition, the GP runs the test, and it turns out negative, and then the GP decides to not send you to a specialist ...
Can anybody infer what the confusion matrix looks like from reading the text?
The company is saying you don't need a specialist, but after bayes theorem (using 90%TN 87%TP and D(A)= 200,000 complication / 29,100,000 diabetes), the chance you have this condition after the machine says you do is 0.83%.
P(H) = .0069
P(D|H) = .87
P(D|H')= .1 (10% chance the test says you have it but you don't)
P(H|D) = 0.057 or about 6% probability you have complication after machine says you do
Ultimately, you will need a doctor somewhere along the diagnostic process so that someone is there to assume liability for incorrect diagnoses.
Ultimately, you will need a doctor somewhere along the diagnostic process so that someone is there to assume liability for incorrect diagnoses.
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).
I wonder if doctors will become like pilots? Ie. Almost redundant, but essential, at the same time.