Could Artificial Intelligence Lead to More Effective Treatment of Depression?

The significant decline in mental wellness has become a dire situation for this country. Depression alone is estimated to cost $210 billion annually and there is a critical shortage of psychiatrists and other mental health specialists. In this time of crisis, artificial intelligence (AI) may offer hope for more effective mental health treatment.

Seeking treatment for depression is not easy, and current treatment options do not make it any easier. Currently, when depression is diagnosed, it can take weeks, months, or even longer in order to find a medication or treatment program that works. In practice, doctors admit that it is trial and error when it comes to treatment selections for these individuals.

Promising new research has been published, however, suggesting that artificial intelligence (AI) could help eliminate these trial and error treatments. A study was conducted where researchers analyzed the brainwaves of patients who were diagnosed with major depression. An electroencephalogram (EEG) was used to measure electrical activity in the brain cortex of these individuals before and after they were administered an antidepressant medication. Researchers subsequently developed a machine-learning algorithm to analyze and use the EEG data to predict which patients would benefit from the administered antidepressant.

What the study found was that 65% of the patients with a particular brainwave signature showed a positive response to the antidepressant treatment. This outcome indicates that there would be a certain confidence level in prescribing antidepressants to individuals who exhibit this brainwave signature. By eliminating the guesswork of prescribing medications, individuals would have faith in the treatment they were receiving—and, more importantly, would experience relief from their depressive symptoms faster!

Most psychiatrists and psychologist offices already have EEG equipment. As a result, it would only be a matter of collecting the brainwave activity and uploading patient data. This test would be quick, is relatively cost-effective, and could provide a high clinical impact on these patients. Of course, there is much testing still to be done before AI could be made fully useful for depression diagnosis and treatment. In the meantime, patients will have to explore their pharmacological options‚ including ketamine infusions—to determine the best treatment for their symptoms.