Скачать или смотреть AI Video Analysis: How AI Is Changing Mental Health Care Between Doctor Visits l Loren Larsen

Patients often hide how they’re really doing, but when AI listens between visits, the truth finally comes out, reshaping mental health care with empathy and precision.

In this episode of An Hour of Innovation podcast, host Vit Lyoshin sits down with Loren Larsen, founder and CEO of Videra Health, to explore how AI in healthcare is transforming behavioral health by capturing what patients actually say and feel outside the clinic, using human-in-the-loop AI to support better care decisions.

They discuss why the most dangerous moments in mental health care often happen between doctor visits, how AI-based check-ins can surface real patient narratives, and why ethical, well-tested AI matters more than ever. The conversation breaks down the limits of score-based assessments, the risks of poorly built AI, and how technology can extend, not replace, clinical judgment. It’s a practical look at mental health technology that’s already being used in real clinical settings.

Loren Larsen is a longtime builder at the intersection of AI, video, and human decision-making. Before founding Videra Health, he served as CTO of HireVue, deploying video AI at a massive scale. In this episode, his experience matters because he’s navigated bias, ethics, and real-world deployment, offering a grounded perspective on what responsible healthcare AI should look like today.

Takeaways
The most dangerous moment in a mental health patient’s life is right after leaving inpatient care.
AI check-ins between visits restore visibility into patient wellbeing when clinicians cannot scale human outreach.
Patients often share more honestly with AI than with therapists because they feel less judged and less pressure to perform.
Mental health scores without narrative (like PHQ-9) miss the “why” behind patient distress.
AI should augment clinical judgment, not replace therapists, especially during high-risk treatment moments.
Generative AI is not ready to safely conduct therapy, particularly in crises.
Model drift can occur from unexpected factors, such as medications or cosmetic procedures, not just bad data.
Poorly built healthcare AI can look legitimate, making it hard for buyers to distinguish safe tools from risky ones.
Ethical healthcare AI requires clear consent, transparency, and human oversight, not just technical accuracy.
The biggest challenge in AI healthcare adoption is balancing speed, safety, and trust in a fast-moving market.

Timestamps
00:00 Introduction
01:35 Videra Health Origin Story
03:02 AI Patient Check-Ins Between Doctor Visits
05:33 Why Human Judgment Still Matters in AI Care
08:49 Gaps in Mental Health Patient Care
12:07 AI vs Human Care in Mental Health
13:23 Testing & Validating Healthcare AI Systems
17:16 Edge Cases, Bias, and AI Model Failure
19:29 Ethical AI in Healthcare
23:33 Why Healthcare AI Adoption Is Hard
25:43 Common Myths About AI in Healthcare
30:02 Lessons from Building Video AI at Scale
34:54 Early Warning Signs in AI Systems
38:31 Advice for First-Time Video AI Builders
42:05 Innovation Q&A

Connect with Loren
Website: https://www.viderahealth.com/
LinkedIn:   / loren-larsen  

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Connect with Vit
Substuck: https://substack.com/@vitlyoshin
LinkedIn:   / vit-lyoshin  
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Podcast: https://www.anhourofinnovation.com/