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: https://www.linkedin.com/in/loren-larsen/

This Episode Is Supported By

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