Self driving cars, human-like robots and robust medical technologies aren’t the only types of artificial intelligence that are making advances in people’s lives. In the mental healthcare industry, clinicians and patients alike are benefitting from advances of machine learning that are making strides in diagnosing mental illness.
Machine learning technology then analyzed risk factors such as childhood adversity, behavioral functioning, early stage psychosis symptoms, genetic information and brain scans using functional magnetic resonance imaging (fMRI), a tool used to model a three dimensional picture of the brain. By virtue of the AI, the algorithm was able to accurately predict 85.9 percent of the time who would develop psychosis from the data—which is significant. The findings from the study revealed just how powerful our total knowledge of psychosis, when put into our computer counterparts, can positively impact diagnostic processes both in speed and accuracy. These results could provide better turn-around times for people waiting to receive a psychotic diagnosis, or otherwise serve as a preventative strategy for warning families with a genetic history of schizophrenia.
This study, and others like it, suggest incredible shifts in the ways we could potentially diagnose and treat mental illness. People who live with schizophrenia often don’t know they have the disorder until it appears in its stereotypical form: a person who is considered “crazy” and “lunatic” from onlookers who poorly understand the disorder. But with these new computational techniques, psychosis may be able to be detected earlier and in younger patients who are at risk for developing psychotic disorders.
The implications of this research are beneficial to the field of mental illness at large. Having a machine accurately predict mental illness gives researchers insight to how mental illness actually functions. Clinicians may learn a thing or two about what makes a mental illness an illness from their soon-to-be machine learning peers.
