In what ways can Viz.ai expand its LVO detection capabilities to identify more subtle signs of large vessel occlusion?

Introduction

Expanding Viz.ai's LVO detection capabilities to identify more subtle signs of large vessel occlusion is a critical challenge that could significantly impact patient outcomes in stroke care. I'll approach this product improvement by analyzing user segments, pain points, and potential solutions, keeping in mind the technical complexities and the high-stakes nature of medical diagnostics.

Step 1

Clarifying Questions

• Looking at the product context, I'm thinking about the current accuracy rates of Viz.ai's LVO detection. Could you share some data on the current sensitivity and specificity of the algorithm, particularly for subtle LVO cases?

Why it matters: Determines the baseline performance and helps identify specific areas for improvement. Expected answer: Current sensitivity is around 85% for clear LVO cases, but drops to 60% for subtle cases. Impact on approach: Would focus on improving detection of specific subtle indicators that are currently missed.

• Considering user behavior, I'm curious about how radiologists and neurologists currently interact with Viz.ai's results. Can you describe the typical workflow and decision-making process when using the tool?

Why it matters: Helps understand how improvements in subtle LVO detection would integrate into existing workflows. Expected answer: Radiologists review Viz.ai results as a second opinion, often relying on it for triage in high-volume settings. Impact on approach: Would focus on enhancing the tool's role in triage and potentially expanding its use in decision support.

• Thinking about external factors, I'm wondering about the regulatory landscape for AI in medical imaging. What are the current FDA requirements for expanding the capabilities of an AI diagnostic tool like Viz.ai?

Why it matters: Ensures that any proposed improvements comply with regulatory standards. Expected answer: FDA requires extensive clinical validation studies for any significant algorithm changes. Impact on approach: Would need to factor in time and resources for regulatory approval in the solution design.

• Considering the product lifecycle, where does Viz.ai stand in terms of market penetration and user adoption? Are we looking to expand our user base or deepen engagement with existing users?

Why it matters: Helps tailor the improvement strategy to the current business objectives. Expected answer: High adoption in major stroke centers, looking to expand to smaller hospitals and increase daily active usage. Impact on approach: Would focus on making the tool more accessible and valuable for a broader range of healthcare settings.