How AI is moving medical devices from diagnostics to powerful learning systems

I'll never forget the 'war room' atmosphere leading up to a major global submission earlier in my career.

Dozens of brilliant scientists and regulatory experts spent weeks manually reviewing thousands of documents, hunting for a single inconsistent data point that could derail the entire application. The process was painstaking, the stakes were immense, and the potential for human error was constant.

That scene crystallized everything that needed to change about how we approach medical innovation. For decades, we've built systems that wait for problems to happen instead of preventing them. We've created static processes that serve their purpose and then go silent, while patients need dynamic partnerships that can learn and adapt with them.

That era is ending.

How AI is transforming medical devices

The medical device industry stands at an inflection point. We're moving from a world focused on diagnostics to one built around prognostics. Instead of devices that simply tell you that you have a condition, we're creating technology that anticipates what's coming next.

The technology and ecosystem are finally aligning to make this vision real across five key areas:

AI agents are handling operational complexity. Companies in the U.S. and Europe are using AI agents for autonomous compliance monitoring, replacing weeks of manual paperwork review with continuous, automated oversight. For example, during year-end insurance resets, when device companies typically scramble to hire temporary staff for the patient support surge, AI agents can now handle these interactions autonomously, helping patients better understand implant options and qualify for programs.

Specialized expertise is reaching more patients. AI is amplifying and extending the knowledge of hyper-specialized clinicians: those who've done years of fellowship training in very specific disease areas. A world-class cardiologist's expertise, once geographically locked to a major medical center, can now inform diagnostic and treatment recommendations in rural or underserved areas. We’re not replacing these specialists. We’re making their knowledge more accessible to patients.

Devices are becoming learning systems. Consider a total knee replacement. Traditionally, you go through the surgery, receive the implant, and that’s the conclusion. Looking forward, implants with monitoring capabilities will be able to track how your body reacts, how you heal, and when it’s safe to return to activities like running or surfing. More importantly,  they will gather data that improves the next version of that device for every future patient. Every implant becomes a teacher for the next one.

Regulators are becoming partners. The FDA's rollout of its internal AI tool, Elsa, signals that regulatory agencies are increasingly embracing AI in their own frameworks. This creates opportunities for transparent, data-driven collaboration where both sides use AI to identify issues quickly, leading to faster, more precise workflows that build trust and accelerate innovation.

Cultural adoption drives success. Teams that have relied on manual processes for decades often view AI as a threat to their expertise. The breakthrough comes when organizations reframe AI as a discovery tool. For example, in my work with a global pharmaceutical company, their regulatory team initially trusted their manual review process implicitly. But when we equipped them with AI that surfaced connections between past submissions that no human could have spotted, identifying a subtle contradiction that could have caused a six-month delay, their perspective shifted. They began to see AI as a capability that elevates their strategic thinking, letting them focus on scientific discovery instead of clerical review.

Building this future together

The transformation I've described requires partners who understand both the technical complexity and regulatory realities of medical devices. This means moving from reactive devices to learning systems, from manual compliance to AI-powered oversight, and from isolated expertise to democratized care.

Google Cloud is working with device companies to build the AI agents that handle compliance monitoring, the imaging capabilities that extend specialist expertise, and the learning systems that turn individual treatments into collective knowledge. Through Vertex AI, we provide the development environment where medical device companies can build and deploy these solutions while meeting the security and compliance standards the industry demands.

This work extends beyond efficiency improvements. We're helping to build a new foundation of trust with regulators, empowering brilliant minds to focus on discovery rather than documentation, and accelerating the delivery of innovative treatments to patients who are waiting.

Every day we accelerate this process is another day we get an innovative treatment to a patient who needs it. That's the metric that truly matters. The medical device industry's AI transformation has begun, and we're committed to ensuring our partners have the strategic insight and capabilities they need to lead this transformation.

Because ultimately, every breakthrough we enable, every process we accelerate, and every innovation we empower is measured by its impact on the patients who are waiting for better health outcomes. That's the future we're building together.