A company called NeuroLex Diagnostics is working on a series of AI tools that can identify telltale patterns and tics in speech to diagnose problems including depression, schizophrenia, Alzheimer's, and Parkinson's.
While seriously intuitive artificial intelligence may have a place in the medical future, for now it's still best to seek a trained medical professional to talk things through. The photographic analysis in the study was more effective than most general practice doctors in diagnosing depression.
Using a photo filter was less common among the individuals with depression diagnoses than among those without it.
The scientists asked volunteers recruited from Amazon's Mechanical Turk crowd sourcing site to share both their Instagram feeds and mental health history. Social engagement involves other people, so we speculated that one rough measure of sociability might be the average number of people that show up in the photos you post.
Depressed users also posted more photos with faces but had fewer people in their photos. The study was designed so that about half of the participants reported having been clinically depressed in the last three years.
"Pixel analysis of the photos in our dataset revealed that depressed individuals in our sample had the tendency to post photos that were, on average, bluer, darker, and grayer than those posted by healthy individuals."But when individuals with a depression diagnosis did use filters, many preferred to filter all the color out of their posts, opting for black-and-white filters such as "Inkwell".
Non-depressed people who use Instagram tended to post pictures which were warm and sunny.
"Fewer faces may be an oblique indicator that depressed users interact in smaller settings", Danforth and Andrew Reece of Harvard University noted, which corresponds to other research linking depression to reduced social interaction-or it could be that depressed people take many self-portraits.
The report said they achieved the high success rate even when restricting themselves to posts made before the depressed individuals were first diagnosed.
The researchers found that the volunteers could distinguish between users with depression diagnoses and those without the condition, to an extent.
But the machine-learning algorithm did a better job, according to the study.
"Obviously, you know your friends better than a computer, but you might not, as a person casually flipping through Instagram, be as good at detecting depression as you think", Danforth said.
Dr Christopher Danforth, study co-author said: "With an increasing share of our social interactions happening online, the potential for algorithmic identification of early-warning signs for a host of mental and physical illnesses is enormous.
But it is a proof of concept of a new way to help people", Danforth said.