When Evaluating Depression, An AI Software is Comparable to Traditional Mental Health Questionnaires

Researchers at The University of Texas at Austin have found that a new artificial intelligence-powered mental health evaluation is just as effective at evaluating depressive symptoms as the “gold standard” questionnaires that are now widely used in the medical field. The nationwide scarcity of mental health specialists may be lessened with the use of the new technologies.

Proved that Aiberry’s AI platform can reliably assess a person’s mental health by examining text, audio, and video cues from an interview conducted by a bot. Furthermore, it revealed no indication of age, ethnicity, or gender prejudice.

Christopher Beevers, co-author of the study, director of UT’s Institute for Mental Health Research, and holder of the Wayne H. Holtzman Regents Chair in Psychology, said, “The idea that depression can accurately be assessed with an AI-driven interview is a really interesting and potentially very important discovery.”

“The ability to monitor depression symptoms on a regular basis at scale could be a game-changer in terms of identifying who might need an intervention. Reliably identifying who is suffering from depression removes an important barrier towards this larger goal.”

According to the current paradigm of measuring depression, people must select the response that “best describes you” from a list of multiple-choice questions in order to rate the frequency and intensity of their own depressive symptoms.

With the help of a digital cartoon named Botberry, users of Aiberry’s app can choose to take a unique AI evaluation that encourages them to describe themselves in their own words. These questions are answered by machine learning algorithms, which then generates a transcript of each response, symptom-level insights, and an overall depression risk score.

“This platform provides the nuance clinicians need that gold-standard forms lack,” said Rachel Weisenburger, a doctoral student in UT’s Mood Disorders Laboratory who led the study. “While standard depression forms can tell you which symptoms are bothering someone, they give you no context. Depression isn’t one size fits all, and the results of this AI-powered interview paint a more complete picture, capturing the messy, human experience of depression in people’s own words.”

The study, conducted in association with Georgetown University Medical Center and the University of Arizona, required 400 volunteers, ranging in age from 18 to 74, to respond to inquiries from Botberry and finish a depression assessment. Based on the answers to the interviews conducted by Botberry, the app then produced a score for the severity of depression symptoms.

Although there was generally agreement between physician evaluations and Aiberry scores, in cases where there were significant differences between Aiberry’s score and the questionnaire form, clinicians undertook a masked review to identify which they were more clinically aligned with. Regardless of the degree of their depression, 88% of respondents said they would like to use Aiberry for mental health monitoring at least once a month.

“As technology and mental health assessment converge, having a clinically validated platform for assessment is crucial to building trust,” said Linda Chung, Aiberry co-CEO. “Our AI-powered platform has the potential to reshape mental health identification and support, marking a huge step toward accessibility for all. At Aiberry, we are committed to fostering mental well-being that empowers individuals and health care professionals.”