I remember a claim that someone was trying to use an ML model to detect COVID by analyzing the sound of the patient coughing.
I couldn't for the life of me understand how this was supposed to work. If the coughing of COVID patients (as opposed to patients with other respiratory illnesses) actually sounds meaningfully different in a statistically meaningful way (and why did they suppose that it would? Phlegm is phlegm, surely), surely a human listener would have been able to figure it out easily.
I don't see why that's a bad idea. If you can also use dogs to detect COVID[1], surely you can build a machine with some sensor that can do the same.
[1] https://academic.oup.com/pmj/article/98/1157/212/6958858?log...
That doesn't really follow. NN models have been able to pick up on noisier and more subtle patterns than humans for a long time, so this type of research is definitely worth a short in my opinion. The pattern might also not be noticeable to a human at all, e.g. "this linear combination of frequency values in the Fourier space exceeds a specific threshold".