Should Owkin prioritize expanding its AI model capabilities or focus on improving interpretability for its existing models?

Introduction

The trade-off question at hand is whether Owkin should prioritize expanding its AI model capabilities or focus on improving interpretability for its existing models. This scenario involves balancing innovation with transparency in the field of AI-driven healthcare solutions. I'll address this complex decision by analyzing the product context, stakeholder impacts, and potential outcomes.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking Owkin's primary focus is on developing AI models for healthcare applications. Could you provide more context on the specific areas of healthcare Owkin is currently targeting with its AI models?

Why it matters: Helps tailor the solution to specific healthcare needs Expected answer: Oncology, drug discovery, and clinical trial optimization Impact on approach: Would influence which capabilities to prioritize

• Business Context: Based on Owkin's business model, I assume they generate revenue through partnerships with pharmaceutical companies and healthcare providers. How critical is model interpretability to these partnerships currently?

Why it matters: Determines the urgency of improving interpretability Expected answer: Highly critical, as partners require transparency for regulatory compliance Impact on approach: Would lean towards prioritizing interpretability if it's a major revenue driver

• User Impact: Considering the end-users of Owkin's AI models, I'm curious about the balance between researchers and clinicians. What's the current split in our user base between these two groups?

Why it matters: Different user groups may have varying needs for interpretability vs. capabilities Expected answer: 60% researchers, 40% clinicians Impact on approach: Would influence the focus on either advanced capabilities or user-friendly interpretability

• Technical Feasibility: Given the complexity of AI models in healthcare, I'm wondering about the technical challenges in improving interpretability. What's our current assessment of the technical feasibility of significantly enhancing model interpretability?

Why it matters: Determines the resources and timeline required for improving interpretability Expected answer: Moderately challenging but achievable with current expertise Impact on approach: Would influence the allocation of technical resources and development timeline

• Resource Allocation: Considering our current team structure, I'm curious about our capacity to pursue both expanded capabilities and improved interpretability simultaneously. What percentage of our development resources are currently allocated to each of these areas?

Why it matters: Helps understand the current prioritization and potential for reallocation Expected answer: 70% on capabilities, 30% on interpretability Impact on approach: Would inform the feasibility of shifting focus without significant restructuring