The MedTech AI Factory: From Algorithm Discovery to Compliant Deployment

Randy Horton

Published on: September 15th, 2026

Your team develops a promising new AI algorithm. Now how long does it take to get that algorithm into your product?

For many MedTech organizations, the challenge is not developing the algorithm itself. It is everything required to move from discovery to production: capturing the right data, determining the regulatory path, validating the algorithm, maintaining evidence of how data was used, and deploying it into a compliant product.

When that process takes a year, your ability to improve the product and respond to new opportunities slows with it.

The Digital Ecosystem & AI Factory provides an end-to-end approach for making AI/ML and Agentic AI development and deployment more repeatable. Rather than treating every new algorithm as a standalone project, organizations can establish the data infrastructure, regulatory processes, validation practices, and deployment capabilities needed to move algorithms through a consistent pipeline.

Join Orthogonal for a webinar on how MedTech organizations can build that pipeline and reduce the time required to bring new AI algorithms from discovery into production.

What You’ll Learn

Build the Data Foundation for Future Algorithms – Understand why raw data, metadata, and other contextual information should be captured intentionally so it can support future algorithm development, training, and validation.
Create a Repeatable AI Development Pipeline – Explore the four phases of the AI Factory: Discovery, Regulatory Determination, Formalization and Validation, and Deployment.
Build Compliance Into the Process – Learn why Good Machine Learning Practices and robust audit trails are essential for documenting how data is used and manipulated throughout algorithm development.
Shorten the Path to Deployment – See how a more deliberate development and deployment model can reduce the time required to bring a new algorithm to market from a year to months.
Plan for External Data and Data Rights – Examine how external sources, such as weather or sleep data, may uncover useful clinical correlations, as well as the legal friction that can make accessing and using that data difficult.

Who Should Attend?

Executive & Business Leaders responsible for digital strategy, AI/ML investment, product portfolios, and accelerating the introduction of new capabilities.
Product, Engineering & R&D Leaders building AI-enabled medical devices, SaMD, connected products, or the digital infrastructure that supports them.
Quality & Regulatory Leaders responsible for creating compliant, traceable processes for AI/ML development, validation, and deployment.

If your organization can develop AI algorithms but still struggles to move them efficiently into production, this session will show how a Digital Ecosystem & AI Factory can create a more repeatable path from data and discovery to validated, deployed software.

Save Your Seat

After the Webinar

Agentic AI Playbook: How MedTech Teams Must Evolve
The role-by-role framework behind the workflow shown in this session.
Automating Compliance with AI in the MedTech SDLC
How compliance evidence, testing and traceability become continuous outputs of development.
Agentic AI insights
All Orthogonal articles, webinars and playbooks on agentic AI in regulated MedTech.

Working on an AI-enabled device?

Orthogonal designs, builds and validates medical device software under design controls, from first prototype to post-market change. Explore AI-enabled medical device software development.

Bernhard Kappe orthogonal software as medical device

Randy Horton, VP of Solutions and Partnerships, Orthogonal