A company in the US, called Lyssn, is developing similar tech. The result could be that more people get better, and stay better.īlackwell and his colleagues are not the only ones chasing this vision. Researchers believe they can use insights from that data to give therapy a long-overdue boost. That gives researchers access to an endless, untapped source of data: the language therapists use. The type of machine learning that carries out automatic translation can quickly analyze vast amounts of language. “And so we didn’t learn very much even over the decades we’ve been doing it.”ĪI is changing that equation. “It takes forever, and the sample sizes are embarrassing,” he says. Zac Imel, who is a psychotherapy researcher at the University of Utah, remembers trying to analyze transcripts from therapy sessions by hand. It’s been virtually impossible to fully quantify what works and why-until now. It can be as much art as science, based on the experience and gut instinct of qualified therapists.

Researchers have tried to study talking therapy for years to unlock the secrets of why some therapists get better results than others. Recovery rates have stagnated and in some cases worsened since treatments were developed. Compared with treatment for physical conditions, the quality of care for mental health is poor. It’s generally deemed crucial for therapist and client to have a good rapport, but it can be tough to predict whether a particular technique, applied to a particular illness, will yield results or not. Despite the fact that therapy has existed in its modern form for decades, there’s a surprising amount we still don’t know about how it works. The success of therapy and counseling ultimately hinges on the words spoken between two people. A new understanding of therapy’s active ingredients could open the door to personalized mental-health care, allowing doctors to tailor psychiatric treatments to particular clients much as they do when prescribing drugs. Ultimately, the approach may reveal exactly how psychotherapy works in the first place, something that clinicians and researchers are still largely in the dark about. Amid a global shortfall in care, an automated form of quality control could be essential in helping clinics meet demand.

The aim is to give therapists better insight into what they do, helping experienced therapists maintain a high standard of care and helping trainees improve.

Ai develops language transcript