Medical diagnostics have to be held to a high standard. False negatives (the app says you're fine when you have cancer) and false positives (the app says you have cancer when you're fine) both have significant consequences.
False negative (24%) means you are not getting the treatment you need as soon as you could. Imagine people delaying a real mammogram for a few years because they have an app that says "all good!".
False positive (35%) means unneeded tests and doctors visits. This is less concerning as a bad outcome, but it does mean more stress , more expense and longer wait times to schedule a doctor visit.
This is an interesting exercise in ML, but there is little chance this app would get approval as a diagnostic tool.
I'm not making any claims about the absolute accuracy of the test or whether it's useful as is. I'm responding to the criticism that (1) the test is less accurate than a lab test and (2) who cares about putting it in a smart phone.
Also, FTA:
> The code will be the deliverable to iSono Health as a baseline model for further algorithm development.
False negative (24%) means you are not getting the treatment you need as soon as you could. Imagine people delaying a real mammogram for a few years because they have an app that says "all good!".
False positive (35%) means unneeded tests and doctors visits. This is less concerning as a bad outcome, but it does mean more stress , more expense and longer wait times to schedule a doctor visit.
This is an interesting exercise in ML, but there is little chance this app would get approval as a diagnostic tool.