More medical device companies are integrating artificial intelligence into their products as the technology advances.
Imaging machines now use deep learning to speed up scans and flag potential lesions for radiologists. AI is also used in wearable devices to detect heart arrhythmias and in software systems to predict the risk of sepsis in hospitalized patients.
As the proliferation of AI in the medtech industry continues, questions remain about the efficacy of these technologies, how they should be regulated and how to mitigate the risk of bias when used in patient care.
How 4 medtech CEOs are using AI
SAN DIEGO — Medtech executives are incorporating artificial intelligence into medical devices, but they are also wading into using the technology for administrative tasks.
At AdvaMed’s The MedTech Conference, company leaders said they expect to incorporate AI into more of their devices. With the recent rise of generative AI, companies are also considering the technology for internal tools, but progress has been slower.
Here’s how four medtech executives are using AI today:
Stryker CEO Kevin Lobo
Lobo said every one of Stryker’s businesses has a digital or AI component. He also expects that Stryker will have a digital division in the future, following the company’s acquisitions of Vocera Communications and Care.ai.
“Five years ago, I would have never thought I’d be buying an AI company,” Lobo said.
With Vocera, Stryker has a technology that provides badge alerts to healthcare workers based on what’s happening in a patient’s room. For example, if a patient is lowering a guardrail to leave their bed, it can send an alert to prevent falls.
Stryker is also developing AI technologies in surgery. The company has a system, called Blueprint, that advises surgeons on the type of implant they should use in an operation and provides a surgery plan.
Projects that use innovation to solve a problem for Stryker’s customers are easy to prioritize, Lobo said, adding that teams “have no trouble finding the money” for these projects.
Figuring out how to use AI for internal productivity is more difficult.
“Frankly, we’re a little bit behind where I’d like to be,” he said, noting that the company is focusing on areas such as customer service and regulatory work.
Insulet CEO Ashley McEvoy
McEvoy, who became CEO of Insulet in April 2025, said the diabetes technology company has used OpenAI’s foundation models for customer care and retention. Insulet uses the technology to provide customer care agents access to company knowledge, which can help with call wait times.
Looking to the future, McEvoy hopes to use Insulet’s data in a precision medicine play to build more personalized algorithms for the company’s insulin pump users.
From a culture perspective, McEvoy said she is working with company leaders to “embrace the good parts of [AI] and not be naive to some of the darker sides,” but still have the freedom to experiment.
Hologic CEO Stephen MacMillan
MacMillan said AI has been helpful with the rise of 3D mammography, which creates more images than 2D mammograms. Looking through so many images can be fatiguing for radiologists. The company uses machine learning to distill the process down to “go look at slide 3 in the upper left quadrant instead of looking at hundreds of slides,” MacMillan said.
The company has started using AI in its breast imaging segment, but plans to roll it out to the rest of its businesses. For example, Hologic has developed a newer tool to support pap smears, which are still often done by cytologists looking at individual slides through microscopes. The company has digitized the process and uses machine learning to flag objects of interest for cervical cancer. Hologic received the FDA’s de novo authorization for the digital cytology system in 2024.
Olympus Chief Strategy Officer Gabriela Kaynor
Four years ago, Olympus started focusing on investing in its data and AI capabilities, but more geared toward the company’s medtech products, Kaynor said. That has culminated in a recent product launch for AI to support colorectal polyp detection, as part of a broader platform to use AI in endoscopy.
In the past two years, Kaynor said, Olympus has explored AI more for enterprise productivity. For example, Olympus built an internal AI tool to help with language translation, which can be a friction point.
Olympus is headquartered in Tokyo, and work is done in Japanese, English and German. Being able to translate data — which might be sensitive — without it leaving the company was a good use case for the technology, Kaynor said. Olympus has been piloting the tool, and the company is getting ready to launch it internally.
“This has been something that I think has reached, really, every corner of our company,” Kaynor said.
Nvidia’s David Niewolny on the future of AI in medical devices
As more medical device companies incorporate artificial intelligence, chip designer Nvidia is playing a central role. The company has struck partnerships with top medtech firms including Medtronic, Johnson & Johnson, GE HealthCare and Philips.
The uses span a breadth of technologies. In imaging, Nvidia has partnered with GE HealthCare around autonomous X-ray and ultrasound solutions, and is working with Philips to develop foundation models, a type of model that can be used for a wide variety of tasks, for MRI machines. In robotics, J&J’s Monarch platform for bronchoscopy uses Nvidia’s computing platform. Nvidia also debuted a new developer framework for healthcare robotics in March 2025, called Isaac for Healthcare. The system features three computers to generate synthetic data to simulate workflows, create virtual environments where robots can safely learn skills, and a platform to deploy applications and for real-time sensor processing.
MedTech Dive spoke with David Niewolny, director of business development for healthcare and medical at Nvidia, about the company’s partnerships and the future of AI in medical devices.
This interview has been edited for length and clarity.
MEDTECH DIVE: What’s the current state of AI in medtech?
DAVID NIEWOLNY: In the last 18 months, we’ve seen this really fast progression in terms of healthcare and medtech adopting generative AI. That’s starting the idea of creating — drafting clinical notes, generating synthetic data for training. Devices are now looking at how can they begin bringing agentic AI into these applications. You’re seeing digital agents being an assistant to the healthcare provider or the patient with automating workflows, driving more context-aware support for clinicians.
Then you get to where we see the future. Where a lot of the AI and innovation is happening is in the idea of physical AI. And that brings us into this role of robotics. The easy one to think of is surgical AI. But there’s a huge number of applications. One specific use case that I talked about in Taiwan [at GTC Taipei] is with some of these more operational robotics. Think of a nurse assistant in terms of delivering medication, bringing different supplies around a hospital, making sure different areas of the hospital are stocked.
You recently announced a partnership with GE HealthCare around autonomous imaging. How does that work?
That was around completely transforming the way that you would look at doing medical imaging in the future. We took two initial use cases, one being the idea of an autonomous X-ray. Think of a future world where you no longer have the X-ray tech. You now have a digital agent that’s essentially checking you in. You walk into a room where there’s another robot, it could be a digital agent, that’s providing you with all of the guidance for where to stand, when to hold your breath and how to position yourself. You stand in one spot, and then the actual machine positions itself.
You can also look at some of the generative AI applications, where it can hand a doctor a full report on everything that it saw in terms of its clinical findings.
In that particular case, now you’re expanding the access to care because you essentially have these fully autonomous systems that are doing medical imaging.
X-ray is one that we announced, and the other one is around ultrasound. In each one of these cases, GE HealthCare is working with us and collaborating on a methodology and tools to build these robotic systems.
What opportunities does Nvidia see in AI in medical devices?
Everything is about building an ecosystem. You look at all of the great work that we can do from accelerating a lot of these applications with AI and now robotics, going from Nvidia direct to a healthcare provider, there’s just too big of a gap. They don’t have the developers in-house to begin building this.
So, then you work backwards in that ecosystem, and you realize it’s the medtech companies that are building out all of these devices and solutions. What we’re looking at doing is, how do we bring all of those components of the ecosystem together, building on a common platform? Computers were not mainstream until Windows opened the door for this huge influx of software.
A lot of this learning we took from another industry: automotive. We essentially needed to create a fully simulated environment, train on that simulated data, take those algorithms and move them down to the edge.
We took those learnings and said, “What other areas are ripe for disruption?” Medtech, specifically, has the most to benefit from this opportunity of having a common platform. But at the same time, it had a big hurdle to jump over. There wasn’t any single company in that ecosystem, as we saw it, that could essentially build out that platform.
I’m hearing more people talk about Agentic AI, a type of AI that can perform autonomous tasks. Why are medtech companies interested in this technology?
Agentic AI has a whole number of applications. A partner of ours, Abridge, leveraged a lot of our technology to do clinical documentation. They’re integrating with major EHRs, and they’re continuing to get more and more hospital users.
You also have some of these agents actually working right alongside a surgeon, where now you have an assistant in the room, where you can begin asking questions, pulling up the patient’s medical records, adjusting some of the devices in the room.
One of our partners, Moon Surgical, actually downloaded their entire instructions for use manual into an agent that a doctor or surgeon can reference for things like setup. Instead of referencing a 1,000-page manual, you can just ask the robot where to be set up, how to be set up and what are the best practices?
Have you faced concerns from clinicians about AI taking their jobs?
Yes, people do get concerned. The key piece is, in almost every one of these cases, we’re augmenting the team members that are already there. There’s a shortage in place. This is actually improving care, as opposed to the narrative around taking people’s jobs. We take a lot of those workloads that the staff sees as either busy work or mundane, and augment them.
There’s always going to be a surgeon involved here, but we can do sub-task automation and actually make some of those tasks easier for a surgeon.
Is your risk-based monitoring strategy ready for the era of AI and sensors?
Risk-based monitoring (RBM) was designed for a different era of clinical trials. The model of periodic site visits, manual source data verification, and centralized trend analysis assumed that the most important information would arrive in batches, to be reviewed after the fact. That assumption no longer holds.
Connected devices, remote sensors, and decentralized trial designs have changed the data environment. Sponsors now receive continuous streams of real-world performance data instead of periodic snapshots. The question facing clinical operations executives is whether their RBM infrastructure is capable of using it.
From snapshots to continuous streams
The shift matters because the risk profile of modern trials has changed in kind, not just degree. In hybrid and decentralized clinical trial (DCT) designs, risk moves away from site-level administration and toward patient device usage and remote data submissions. Fixed monitoring schedules designed around pre-set site contacts cannot adequately address the deviations that show up in real-time data between visits.
Real-time access allows sponsors and CRAs to detect irregular patterns like missed submissions, incorrect device usage, and anomalous sensor readings as they occur, allowing them to direct intervention resources precisely where they are needed. Under both FDA guidance and EU MDR’s increasingly stringent post-market clinical follow-up (PMCF) requirements, that kind of continuous, adaptive oversight is no longer optional.¹
AI as a force multiplier — with limits
AI and machine learning tools offer the scalable oversight necessary to process high-volume continuous data streams and flag signals that would be normally be lost in manual review. Analysis suggests that AI-enabled monitoring has strong potential to redirect resources toward high-performing centers and accelerate site-level decision-making.² However, AI can make mistakes (e.g., false positives), so the most defensible approach is human-guided automation: AI detects irregularities and potential safety issues, but no action is taken without clinical expert confirmation.
Governance is the real constraint
For most organizations, the barrier to modernizing RBM is governance. AI tools require high-quality, structured data to function reliably. Teams that lack clear protocols for human oversight, defined accountability at the investigator level, and infrastructure to handle continuous data streams will find that adding technology compounds the problem rather than solving it.
Before live deployment, leading sponsors use three approaches to stress-test their frameworks without exposing sensitive patient data:³
- Synthetic data modeling — simulated datasets allow teams to test monitoring strategies and filtering algorithms before live integration
- Data filtering — separating signal from noise in real time ensures monitoring efforts focus on meaningful deviations, not data volume
- In silico optimization — computer modeling predicts anatomical responses and adverse event timing, helping sponsors refine inclusion criteria
The executive takeaway
RBM is no longer a clinical operations function. It is a core component of life cycle management, PMCF strategy, and regulatory compliance. Organizations not treating it as such are carrying risk they may not see until it reveals itself in a regulatory finding or a trial that cannot generate defensible evidence.
Modernizing RBM means addressing four organizational realities:
- Study oversight has evolved — your infrastructure needs to handle modern hybrid and decentralized models, not just periodic site visits
- Manual review is a signal for change — extra manual steps or bypassed remote data opportunities suggest your data management strategy needs a refresh
- Governance is nonnegotiable — AI and ML drive efficiency, but clinical context and data quality are the investigators’ responsibility
- Partnership matters more than software — you need partners who understand MedTech device complexity, not generic platforms built for pharma
References
1 FDA Guidance. (2024). Digital Health Technologies for Remote Data Acquisition in Clinical Investigations.
2 Applied Clinical Trials. (2025). Modernizing Clinical Oversight: The Shift to Adaptive Monitoring.
3 Journal of Clinical Innovation. (2025). The Role of In Silico Modeling in Modern Trial Design.
FDA exempts more wearable, AI features from oversight
The Food and Drug Administration released two final guidance documents in January 2026 that would loosen regulations for certain types of wellness and software products.
FDA Commissioner Marty Makary announced the changes at the Consumer Electronics Showcase. Makary, in a video posted on X, said the changes would “promote more innovation with AI in medical devices.”
Wellness exemptions for blood pressure, blood glucose
The first guidance clarifies the FDA’s thinking on what constitutes a wellness device. It offers broader leeway to wearables that provide readings around heart rate, blood pressure and blood glucose, so long as they are intended solely for wellness purposes.
In examples provided in the guidance, the FDA said a wrist-worn wearable that tracks metrics including sleep, pulse rate and blood pressure would fall under a general wellness claim, provided the product has validated values for blood pressure.
The guidance appears to contradict a warning letter the FDA sent to wearable company Whoop in 2025 for rolling out a blood pressure feature without authorization. At the time, the agency said that blood pressure is inherently related to a medical diagnosis.
A spokesperson for Whoop applauded the new guidance in an emailed statement, saying it clarifies that Whoop can provide blood pressure insights and wellness metrics when designed for non-medical purposes. The spokesperson said the changes should “help resolve long-standing uncertainty about the boundary between providing wellness insights and medical diagnosis and treatment.”
Another example was for a wearable intended to provide blood glucose estimates to monitor nutritional impacts, and is explicitly contraindicated for use by people with diabetes and pre-diabetes. According to the guidance, this would fall under a general wellness claim, but would not count as a low-risk product if the device used microneedle technology to provide the estimates.
In 2024, the FDA warned consumers not to use smart watches or smart rings that claim to measure blood sugar without piercing the skin.
“In the past, the FDA has permitted some measurements like pulse rate and O2 saturation in some wellness products, but not others,” Makary said in a speech at CES. “It didn’t always make a lot of sense from the outside.”
Tom Hale, CEO of Oura, maker of a wearable ring that is working on a blood pressure feature, welcomed the changes in a LinkedIn post.
“As wearables continue to evolve, modern regulation that recognizes the difference between early awareness and medical diagnosis is critical,” Hale wrote.
Less regulation of clinical decision support software
In a separate guidance, the FDA unveiled significant changes to how it regulates clinical decision support tools. The biggest change is to a section describing how the FDA interprets whether software is providing recommendations to healthcare providers. Software that provides a sole medical recommendation can now be exempt from regulation. Under a previous guidance, it would have been considered a medical device.
For example, according to the new guidance, software that predicts a patient’s risk of future cardiovascular events based on their weight, smoking status, blood pressure and lab tests would be exempt from FDA enforcement. However, if the same test was intended to predict the risk within 24 hours, or used genomic data, it would be considered a medical device.
Another example considers the use of software to summarize radiologists’ findings, a concept that firms are testing using generative artificial intelligence. A software function that analyzes a radiologist’s report to generate a summary with specific diagnostic recommendations would be exempt. However, if the software directly analyzed an image to create the report, it would still be regulated by the FDA.
The guidance also spells out what information device makers must provide to clinicians so they can review the basis of the software’s suggestions. Software intended for critical, time-sensitive tasks would still be considered a device, and companies should provide a description of the underlying algorithm, validation and required input information, the FDA said.
The pair of guidance documents offers some insight into the Trump administration’s approach to AI. In past months, the administration has pushed for a deregulatory approach, but with few details. The Department of Health and Human Services issued a request for information to the healthcare industry in December 2025, asking for feedback on how the department can help speed adoption of AI.
Both documents were published without a public comment period, an approach the Trump administration has recently taken for major policy changes.
CDRH Director Tarver previews AI guidance at AAMI event
Michelle Tarver, director of the Center for Devices and Radiological Health, provided an update on digital health and artificial intelligence policy at the Association for the Advancement of Medical Instrumentation’s neXus conference.
Tarver said the CDRH plans to issue final guidance on AI lifecycle management, and told attendees to watch for the center’s thinking following two advisory committee meetings on generative AI.
The Food and Drug Administration published draft guidance on AI lifecycle management in January 2025. The document outlines best practices to ensure AI-enabled devices are safe and effective, and how developers should address transparency and bias.
All AI algorithms are required to be trained on data that reflects the intended use population and should be validated in the intended use population, Tarver said.
The draft guidance also addresses postmarket monitoring after an AI-enabled device is deployed in the real world, knowing that this technology can hallucinate, bias can come into play and models may drift, or become less accurate over time.
“All of that can impact the output that those technologies are generating and impact patient outcomes as a result,” Tarver said.
The FDA is currently looking at comments from the draft to issue a final guidance, Tarver said.
The CDRH director also addressed generative AI, a technology that can generate text, images, software code and other outputs.
The CDRH’s Digital Health Center of Excellence held two advisory committee meetings on the topic: one in 2024 on how the agency should regulate generative AI, and another in 2025 on digital mental health devices, including chatbots that may or may not be overseen by a clinician.
“We are taking all the feedback that we heard, and I encourage you to look toward the end of this year for some initial thoughts,” Tarver said.
The update provided clarity as the Trump administration has focused on AI adoption and deregulation, but with little information on what that means for healthcare.
In 2025, after President Donald Trump was sworn in for his second term, the CDRH issued just one final guidance related to AI, on predetermined change control plans. Meanwhile, the agency grappled with staff cuts, which affected experts in AI and digital health, although some employees were reinstated.
In 2026, the new year started off with two unannounced changes to digital health guidance documents that exempted some types of wellness features and clinical decision support tools from device regulations.
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