Generative AI Impact & Use Case in MedTech

By: Travis Smith — Founder and Managing Director of Square-1 Engineering
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How can AI be responsibly and effectively integrated into regulated industries like MedTech to speed up decision making, reduce administrative burden, and strengthen compliance?

This is the question that was ask of attendees on December 2, 2025, at Enginius.AI private symposium which brought together regulatory, quality, product development, and digital health leaders from across the medical device sector. The breakfast event featured industry expert panelists including Sandeep Mehta, Stan Rowe, and Todd Abraham.
As the discussion got underway attendees came quickly to a unified consensus: AI holds enormous potential—but practical, validated use cases matter far more than hype.
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​The Origin of Enginius.AI
The organization putting on this event, Enginius.AI, opened up with a short history lesson on what got them started and how they transitioned their product to what it is today. Sandeep Mehta, Enginius.AI’s CTO, shared the company’s beginnings painting a picture of an AI platform originally built for aerospace, aimed at accelerating regulatory approvals by connecting otherwise siloed engineering documents. Early lessons made it clear that AI alone wasn’t enough — success required a connected ecosystem tailored to engineering needs, where traceability and validation are crucial. The next step for the organization was taking their lessons learned and adapting their technology to be applicable to the medical device industry. 

Where AI Can Deliver Now
Attendees—spanning RA/QA leaders, AI startup founders, and product development specialists—expressed a strong appetite for actionable AI solutions. They wanted tools that reduce paperwork, accelerate product development, and improve regulatory documentation quality, rather than abstract theory.
Stan Rowe, CEO of Nidus Biomedical, emphasized AI delivers the greatest value in repeatable, documentation-heavy workflows: Design History Files, labeling, complaints handling, and SOP management. He estimated early adopters could cut document preparation time by 40–70% and boost overall productivity by 20–30%.  Rowe also stressed “AI should augment—not replace—human decision-making.”  For regulated companies, trust is key. AI must operate behind firewalls, integrate into existing processes, and provide auditable outputs in order for it to provide valuable ROI in MedTech applications.

Picking the Right Problems for AI
Discussions highlighted that AI excels with well-defined, data-rich tasks. For common products with predictable documentation patterns, AI can automate DHF development, risk analysis, and evidence organization. Novel or highly innovative work yields smaller gains, reinforcing the need to manage expectations. Overall, the highest near-term ROI comes from automating administrative tasks, freeing engineers and SMEs to focus on complex technical thinking.
Todd Abraham, MedTech COO, addressed the challenge of integrating data across multiple systems—ERPs, QMS platforms, PLMs, and legacy databases. AI can serve as an agnostic layer above these systems, aggregating data for CAPAs, V&V, or submissions without overhauling infrastructure. The key is assigning structured, measurable problems to AI rather than ambiguous tasks.
Kenny Dang of Terumo Neurovascular noted that regulators might one day use AI to scrutinize submissions at speeds humans cannot match. If so, AI adoption will not just be advantageous—it will be necessary to maintain compliance and stay competitive.

The Window for Early Adaption & Advantage
Julian Husband, Enginius.AI’s CRO, summed it up: “We are our own competition.” Companies that adopt AI early can move faster, build cleaner documentation, and stay aligned across RA, QA, clinical, and product development. Late adopters risk falling behind or overbuilding processes AI could streamline.

Key Takeaway
The takeaway from the symposium: AI adoption in RAQA and product development is accelerating. Early adopters gain speed, insight, and quality, while laggards risk being constrained by outdated workflows. Success will hinge on trust, validation, workflow integration, and balancing automation with human oversight. Picking the right use case to use AI within a medical device operation is critical to seeing success while balancing an appropriate ROI.

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