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New research is showing that it is possible to detect Parkinson's symptoms by using algorithms to detect changes in voice recordings. So it could be possible that your iPhone would one day be able to diagnose you.

Parkinson's, a degenerative disorder of the central nervous system, is usually diagnosed through analysis of symptoms and expensive medical imaging to rule out other conditions. There is no concrete method to detect the disease.

Max Little, from the University of Oxford, is developing a software that learns to detect differences in voice patterns in order to spot distinctive clues about Parkinson's. He explains his plan to the BBC:
"This is machine learning. We are collecting a large amount of data when we know if someone has the disease or not and we train the database to learn how to separate out the true symptoms of the disease from other factors."
Little developed an algorithm using data from 50 patients with Parkinson's who had their voices recorded once a week for six months. He was able to develop the algorithm to detect changes in voice purely associated with Parkinson's. The software accurately picked out Parkinson's patients from a random population with 86 percent accuracy.

Little is going to take it a step further and he's announced at TEDGlobal that the project is extending and is inviting members of the general public to phone in and leave voice recordings to help him improve the software. The aim is to collect 10,000 voices and people from around the world are encouraged to contribute.

If the technology proves successful, Little hopes to roll it out to use by doctors in two years and is adamant that it will help in the diagnosis of the disease.
"We're not intending this to be a replacement for clinical experts, rather, it can very cheaply help identify people who might be at high risk of having the disease and for those with the disease, it can augment treatment decisions by providing data about how symptoms are changing in-between check-ups with the neurologist."
[Parkinson's Voice Initiative via BBC]