Patients with anxiety feel more tension in their bodies, which can also change the way their voice sounds, she said. “They tend to speak faster. They have more difficulty breathing.”

These technological advances come at a time when the need for mental health care is particularly acute: According to a report from the National Alliance on Mental Illness, one in five adults in the United States experienced mental illness in 2020. And the numbers continue to climb.

Other technologies add a potentially helpful layer of human interaction, like Kintsugi, a company based in Berkeley, Calif., that raised $20 million in Series A funding earlier this month. Kintsugi is named for the Japanese practice of mending broken pottery with veins of gold.

Although A.I. technology can’t address the scarcity of qualified mental health care providers — there are not nearly enough to meet the country’s needs, said Dr. Bentley — there’s hope that it may lower the barriers to receiving a correct diagnosis, assist clinicians in identifying patients who may be hesitant to seek care and facilitate self-monit... See more

**Note:** These concerns are the same as in all applications of AI: bias, privacy, interpretability

Founded by Grace Chang and Rima Seiilova-Olson, who bonded over the shared past experience of struggling to access mental health care, Kintsugi develops technology for telehealth and call-center providers that can help them identify patients who might benefit from further support.

With depressed patients, Dr. Espinola said, “their speech is generally more monotone, flatter and softer. They also have a reduced pitch range and lower volume. They take more pauses. They stop more often.”

By using Kintsugi’s voice-analysis program, a nurse might be prompted, for example, to take an extra minute to ask a harried parent with a colicky infant about his own well-being. 

Today, these types of vocal features are being leveraged by machine learning researchers to predict depression and anxiety, as well as other mental illnesses like schizophrenia and post-traumatic stress disorder. The use of deep-learning algorithms can uncover additional patterns and characteristics, as captured in short voice recordings, that migh... See more