Impact AI Identifying Mental Health Challenges through Speech with Rima Seiilova-Olson from Kintsugi
Over the past few years, there has been a concerning rise in rates of depression, anxiety, and other mental health disorders, especially among adolescents and young adults. In addition, the current state of our national healthcare system is not set up to offer equitable access to all, depriving a huge portion of the population of the help they need. The new platform, Kintsugi, has been taking important steps to address these shortcomings by developing AI learning models that detect signs of depression and anxiety from speech samples.
Today on the show I welcome Rima Seiilova-Olson, Co-Founder and Chief Scientist at Kintsugi, to talk about the current state of mental health care and what Kintsugi is doing to offer support to the many individuals who need it. I talk with Rima about her difficult experience with postpartum depression, her subsequent struggle to access mental health care, and how these events (combined with her expertise as a software engineer) led her to co-found Kintsugi. Rima goes on to explain how Kintsugi can be used as a tool by mental health professionals, and the benefits of incorporating it into clinical workflows. We also discuss some of the biggest challenges of working with speech data, the systems they are putting in place to combat bias, and how psychiatrists are helping them validate their speech data. To learn more about this incredible technology, and the life-altering impact it could have, be sure to tune in today!
Key Points:
- Get to know today’s guest, Rima Seiilova-Olson, Co-Founder and Chief Scientist at Kintsugi.
- The set of circumstances that inspired Rima to create Kintsugi.
- What it means to bring quantifiable and scientific measures into the field of mental health.
- How Kintsugi detects signs of depression and anxiety from speech samples.
- Understanding the benefits of incorporating this technology into clinical workflows.
- The integral role of machine learning in this technology.
- The limitations of traditional healthcare systems.
- How Kintsugi is helping more people gain access to mental health care.
- Why Kintsugi uses psychiatrists to collect and validate data.
- The biases that can occur in models trained on speech data.
- The measures Kintsugi has put in place to mitigate these biases.
- Some of the biggest challenges of working with speech data.
- Rima’s insights on the potential financial, clinical, and emotional impacts of this new technology.
- Rima’s advice to other founders of AI-powered startups.
- Why it’s so important to combat the trend of digital health companies prioritizing financial gain over clinical impact.
- The expected impact of Kintsugi over the next five years.
Quotes:
“My co-founder and CEO, Grace Chang, and I put our heads together and decided to start by bringing quantifiable and scientific measures into the field of mental health. Which has mainly been qualitatively and subjectively driven for many decades.” — Rima Seiilova-Olson
“Instead of these clunky tools, we give [healthcare providers] seamless tools that can analyze depression or anxiety in their patients.” — Rima Seiilova-Olson
“I think the main impact that we, as founders, are excited about, is the emotional impact of our technology. Which is not quantifiable, but we believe it is going to be immense.” — Rima Seiilova-Olson
“By connecting patients to access, we’re going to have a profound effect on society. [A society] that is observing skyrocketing trends in the rates of depression and anxiety, especially among young adults and adolescents.” — Rima Seiilova-Olson
“We’re observing interesting trends where certain companies prioritize financial gains and revenue over clinical impact and the clinical outcomes for the patient. And those stories not only affect that one startup, it affects the whole industry.” — Rima Seiilova-Olson
“I think every single AI startup in healthcare needs to prioritize the ethical implications of their product. So that as an industry, we cover some of the damage that has been done by some of our colleagues.” — Rima Seiilova-Olson
Links:
Rima Seiilova-Olson on LinkedIn
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