Over the last few years, AI and machine learning have moved from research projects into the core of the medical industry. Models now influence diagnosis, treatment planning, surgical systems, and patient outcomes.
That shift changes the cost of uncertainty.
This year, regulators won’t just evaluate what an AI or ML model does. They will increasingly scrutinize whether a manufacturer can reproduce a model’s behavior, explain how it evolved, and demonstrate that changes were intentional, validated, and controlled.
A useful example comes from Medtronic’s Hugo robotic-assisted surgery platform. Hugo was positioned as a next-generation surgical system, yet its path to broad regulatory clearance took years longer than initially expected. Originally targeted for U.S. launch in late 2022, Hugo received FDA clearance in December 2025. Medtronic publicly discussed the delays, citing the need for additional validation, supply chain challenges, and regulatory requirements.
Regardless of the precise internal causes, the lesson for the medical industry is clear:
When regulators ask deeper questions and organizations cannot reproduce past model states on demand, time and cost escalate quickly.
Every round of additional validation:
• Extends R&D and regulatory spend
• Delays product revenue
• Creates opportunity cost as competitors advance
• Redirects engineering effort towards reconstructing evidence rather than innovating
This challenge is amplified by how most AI and ML models are built today.
Models are retrained continuously as new clinical data arrives and algorithms improve. But training pipelines often rely on mutable datasets, overwritten records, and “latest” references. Months later, when performance shifts or questions arise, teams may know which data sources were involved, but still be unable to reproduce the exact training dataset and model behavior.
Lineage helps identify what changed.
Reproducibility proves it.
Without reproducible data snapshots, organizations are forced into expensive, manual reconstruction under regulatory pressure.
The organizations best prepared aren’t slowing innovation. They’re designing AI and ML workflows where every model is tied to an immutable, reproducible version of training data, and every change can be replayed, validated, and defended.
In the medical industry, regulation doesn’t just raise expectations for safety. It raises the cost of non-reproducible AI.
And reproducibility is becoming the difference between controlled progress and costly delay.
Ready to build reproducibility into your AI/ML pipelines? Medical device companies use lakeFS to maintain immutable data snapshots, track lineage across model iterations, and respond to regulatory inquiries with confidence. Talk to one of our experts to learn how lakeFS can help your team meet regulatory requirements without slowing down innovation.
