Discovery & Foundational Research

Focus on understanding the clinical problem and market context. Work with clinicians to identify unmet needs, analyze competitors to find gaps, and evaluate regulatory, ethical, and data privacy requirements early to shape strategy.

Ideation, Prioritization & Value Definition

Once you have a solid understanding of the landscape, you can begin to define your product's purpose and prioritize features based on impact and feasibility. Define AI Use Cases: Brainstorm and refine potential AI applications. For a medical device, this could be anything from a diagnostic algorithm for medical imaging to a predictive tool for patient deterioration. Prioritize Based on Impact: Rank your use cases based on their potential to deliver clinical value, business value (e.g., return on investment), and technical feasibility.

Define Success Metrics: Establish clear, measurable metrics for each feature. For an AI product, this includes not just business KPIs but also clinical performance metrics like algorithm accuracy, sensitivity, and specificity.

Technical Validation & Data Strategy

Before committing to a full-scale build, you must validate the technical foundation and ensure you have a viable data strategy.

Data Acquisition & Curation: Identify the data you need to train and validate your AI model. This is often the most significant challenge. Create a strategy for securing, cleaning, and labeling high-quality, relevant data.

Proof of Concept (PoC): Build a small-scale, minimal-viable product (MVP) or PoC to test your core hypothesis. This validates that the technology can solve the identified problem without a significant investment.

Initial Model Development: Train a preliminary AI model and evaluate its performance against your defined success metrics.

Roadmap Development & Lifecycle Planning

This is where you formalize your strategy into a living document that guides your team and stakeholders.

Phased Approach: Develop a phased roadmap that outlines key milestones. A typical roadmap includes a pre-market phase (proof of concept, regulatory strategy), a market entry phase (clinical trials, regulatory submission), and a post-market phase (post-market surveillance, feature updates, continuous monitoring).

Risk Management: Integrate risk management directly into your roadmap. Identify technical, regulatory, and ethical risks for each phase and develop mitigation plans.

Continuous Feedback Loop: The roadmap is not static. Establish a feedback loop with clinicians, patients, and regulators to continuously refine and adapt the product strategy based on real-world data and evolving needs.