One of the key advantages of AI is how quickly and thoroughly AI programs can achieve  a broad range of healthcare research tasks.

For example, a recent article in the UK Independent described a study by researchers at the Penn Medicine Centre for Digital Health which explored the capacity of AI programs to review Facebook posts for “linguistic red flags” that could be a sign of depression.

“In early tests,” the article explained, “the machine learning algorithm performed as well as existing screening questionnaires which are used to identify depression – but it has the advantage of being able to run ‘unobtrusively’ in the background.”

The researchers note the new tool could one day be used to help screen for mental health conditions by detecting early warning signs, including mentions of loneliness or isolation such as “alone”, “ugh” or “tears” as well as the timing and length of posts. 

“Social media data contain markers akin to the genome,” said Dr Johannes Eichstaedt, one of the senior authors of the study and a co-founder of the World Well-Being Project at the University of Pennsylvania. “With surprisingly similar methods to those used in genomics, we can comb social media data to find these markers. Depression appears to be something quite detectable in this way; it really changes people’s use of social media in a way that something like skin disease or diabetes doesn’t.”

For the study, the researchers used data from the Facebook profiles of 683 people who had consented to share their digital archives, including 114 people who had been diagnosed with depression.

The researchers found their program was most accurate when they focused on social media cues in the six months prior to a depression diagnosis, and they wrote that it could help flag the onset of depression in people at risk particularly when working with other forms of digital screening.

“There’s a perception that using social media is not good for one’s mental health,” noted H. Andrew Schwartz, an associate professor of computer science and principle investigator of the study. ”But it may turn out to be an important tool for diagnosing, monitoring, and eventually treating it.”