Dr Isaac Galatzer-Levy: How Tech Could Help Identify and Intervene in Mental Illness

Isaac Galatzer-Levy, PhD, assistant professor in psychiatry and bioinformatics, NYU School of Medicine, and vice president of clinical and computational neuroscience, AiCure, said that technological solutions like artificial intelligence (AI) can help identify and monitor signs of mental illness so patients can be connected with the help they need.

Transcript

What are some future avenues in psychiatry that could be addressed using AI/machine learning?

Where I think the largest unmet need is in how we actually define and track people with mental health issues. So, what does it mean to be mentally ill? How can we see that out in the wild? How can we see when a patient is not doing well, such that we can intervene to really reduce the hard outcomes that affect their lives?

So I’ll give you an example: If you think about patients with schizophrenia or bipolar disorder or even depression, their disorders really have a cyclic nature to them where, yes, they’re sick and they need to be maintained on medication, but they have episodes that are particularly acute that have huge negative consequences in their life, lead to hospitalization, which really derails multiple aspects of their life and is very hard to come back from.

So I think that there’s a huge opportunity for technology to spot that risk early on to close the loop in terms of interventions and really move both treatment and monitoring outside of the hospital system and into the world. If we can spot patients when they’re doing poorly early on, we can intervene with them and help them. There’s actually an enormous amount of resources available to do that; we’re just not able to link the resources with the people who need them. And I see technology being a real way to achieve that.

I think that the role that AI in particular can play is in how do we actually monitor and understand how a patient is doing. So most of the work that I do right now at AiCure but also in my lab at NYU is focused on how do we identify signals of mental health functioning based on facial recognition, voice, and speech.

So if you think about a patient with depression, there’s a clear signal for what a depressed patient looks like. They talk slowly, they show a lot of negative affect on their face, they use a lot of negative content in their speech, so can we capture that kind of information and understand how a patient is doing on a day-to-day basis, and then intervene when necessary or even deploy treatments as necessary.