In addition to the schizophrenia screener, an idea that earned Schwoebel an award from the American Psychiatric Association, NeuroLex is hoping to develop a tool for psychiatric patients who are already being treated in hospitals. Rather than trying to help diagnose a mental disorder from a single sample, the AI would examine a patient’s speech over time to track their progress.

For Schwoebel, this work is personal: he thinks this approach may help solve problems his older brother faced in seeking treatment for schizophrenia. Before his first psychotic break, Schwoebel’s brother would send short, one-word responses, or make cryptic to references to going “there” or “here”—worrisome abnormalities that “all made sense” after his brother’s first psychotic episode, he said.

According to Schwoebel, it took over 10 primary-care appointments before his brother was referred to a psychiatrist and eventually received a diagnosis. After that, he was put on one medication that didn’t work for him, and then another. In the years it took to get Schwoebel’s brother diagnosed and on an effective regimen, he experienced three psychotic breaks. For cases that call for medication, this led Schwoebel to wonder how to get a person on the right prescription, and at the right dose, faster.

To find out, NeuroLex is planning a “pre-post study” on people who’ve been hospitalized for mental disorders “to see how their speech patterns change during a psychotic stay or a depressive stay in a hospital.” Ideally, the AI would analyze sample recordings from a person under a mental health provider’s care “to see which drugs are working the best” in order “to reduce the time in the hospital,” Schwoebel said.

If a person’s speech shows fewer signs of depression or bipolar disorder after being given one medication, this tool could help show that it’s working. If there are no changes, the AI might suggest trying another medication sooner, sparing the patient undue suffering. And, once it’s gathered enough data, it could recommend a medication based on what worked for other people with similar speech profiles. Automated approaches to diagnosis have been anticipated in the greater field of medicine for decades: one company claims that its algorithm recognizes lung cancer with 50 percent more accuracy than human radiologists.

The possibility of bolstering a mental health clinician’s judgment with a more “objective,” “quantitative” assessment appeals to the Massachusetts General Hospital psychiatrist Arshya Vahabzadeh, who has served as a mentor for a start-up accelerator Schwoebel cofounded. “Schizophrenia refers to a cluster of observable or elicitable symptoms” rather than a catchall diagnosis, he said. With a large enough data set, an AI might be able to split diagnoses like schizophrenia into sharper, more helpful categories based off the common patterns it perceives among patients. “I think the data will help us subtype some of these conditions in ways we couldn’t do before.”