Could Artificial Intelligence Help With Early Diagnosis of Schizophrenia?

Patients may present with hallucinations (sometimes), social withdrawal (maybe) or delusions (not always). More generally, they just sound unlike themselves. Clinicians rely on their expertise and subtle cues to determine how different patients’ speech is, along with which symptoms appear over time, to justify leaning toward schizophrenia rather than another mental illness.

This leads to delays in diagnosis. Americans with psychotic disorders—more than three million of whom have schizophrenia—receive a diagnosis a year and a half, on average, after their first symptoms appear. Researchers are now investigating whether artificial intelligence could improve diagnosis and care by listening to and analyzing what clinicians can’t hear or quantify, even if the software is working off just a few minutes of conversation.

A.I. won’t make its grand entrance into the clinic tomorrow. But it’s being hailed as the new frontier in psychiatric care, one that could enable early, accurate detection and personalized monitoring of illnesses based on indistinct symptoms. National Institute of Mental Health for 13 years and founded and advised on several mental health startups.

Schizophrenia is a disorder that interferes with people’s perception of reality, their thinking and their emotional regulation. It affects about 23 million people worldwide and is usually diagnosed between the late teens and early 30s. No one knows what causes it, but research suggests it could be a combination of genetics, environment, brain chemistry and substance use.

Clinicians stress the importance of detecting the disorder as early as possible, because the longer it is left untreated, the poorer the response to treatment and the greater the risk of brain tissue loss, worsening symptoms and suicide. But psychiatrists often make errors. Part of the problem is that diagnosis currently depends on subjective assessments.

Based on what a patient says and how they say it, clinicians fill out one of several different rating scales to rank the severity of symptoms and arrive at a diagnosis. But the process is difficult to standardize, and clinicians’ scores for a given patient can differ by 30 to 50 percent. Artificial intelligence could help to automate the process, making diagnosis both speedier and more accurate.