It often feels like there’s no corner of healthcare that artificial intelligence hasn’t touched, and the medtech industry is no exception.
From diagnostic imaging to patient monitoring, AI-enabled technologies are changing how healthcare professionals diagnose conditions, make decisions, and deliver care. And as the number of FDA-authorized AI medical devices continues to grow, the field may very well be entering a new phase of innovation.
But the story of medtech AI is about more than the pace of innovation. It’s also about where that innovation is happening, how it’s being translated into clinical practice, and what increasingly sophisticated AI capabilities could make possible for both patients and providers.
Let’s take a closer look at three trends influencing AI in medical devices and what they could mean for the future of the industry.
FDA approvals of AI devices continue to rise annually
The number of AI-enabled medical devices authorized by the U.S. FDA has grown significantly over the past decade. In 2015, the agency approved just six devices for marketing in the country. In 2025, it authorized 235. And as of June 2026, more than 1,600 such devices received FDA authorization since 1995.
Fig 1. Number of AI-enabled medical devices authorized by the FDA, 1995–2026. Note: 2026 data are partial through June 29. Source: The U.S. Food and Drug Administration
The FDA’s authorization data illustrates just how quickly the market has expanded. After relatively modest activity in the 1990s and early 2000s, the number of AI-enabled devices receiving authorization began accelerating significantly over the past decade.
While agency approval is an important milestone, it doesn’t necessarily mean a device has been widely adopted by healthcare organizations or demonstrated measurable improvements in patient outcomes.
In fact, there’s evidence that more work is needed to bridge the gap between innovation and implementation. A 2025 study published in JAMA Network Open examined publicly available information for 903 FDA-authorized AI-enabled medical devices. Researchers found that clinical performance studies were reported for only about 56% of the devices, with relatively few prospective and randomized test designs among those identified. The study also identified gaps in the available evidence needed to assess how reliably devices could perform across different patient populations.
These findings don’t necessarily mean there is a problem or that the technologies are ineffective. Rather, they highlight the importance of clinical validation, ongoing performance monitoring, and evidence that AI-enabled medical devices can deliver consistent results beyond controlled testing environments.
That being said, in 2025, the FDA did issue draft guidance addressing the management of AI devices throughout their product lifecycles. The agency also finalized guidance on predetermined change control plans, providing manufacturers with recommendations for planning and implementing certain device modifications while maintaining safety and effectiveness.
Altogether, the implications are significant for medtech companies and extend beyond maintaining regulatory compliance. AI will only become more sophisticated over time, meaning that successfully bringing devices that use AI into clinical practice will require a deeper understanding of how healthcare professionals will use them, the challenges they must address, and the value they can deliver.
Radiology accounts for the bulk of AI medical device approvals
Of all the medical specialties embracing AI, radiology stands out as the clear leader. Radiology accounts for more than three-quarters of all FDA-authorized AI-enabled medical devices, far outpacing other specialties like cardiovascular medicine and neurology. This illustrates how AI is changing the way radiologists process images and speed up workflows.
Fig 2. Number of AI-enabled medical devices by primary FDA review area. Source: The U.S. Food and Drug Administration
Why has radiology emerged as such a prominent area for AI innovation?
Much of the answer lies in the nature of medical imaging itself. Radiologists work with enormous volumes of digital information, examining X-rays, CT scans, MRIs, and other imaging studies to identify abnormalities, diagnose conditions, and guide treatment decisions. AI technologies are particularly well-suited to such tasks, being able to recognize complex patterns, analyze visual data, and automate repetitive tasks.
Major manufacturers such as GE HealthCare, Siemens Healthineers, and Philips have incorporated AI capabilities into medical imaging technologies. Specialized developers, including Aidoc and Viz.ai, have also built solutions around helping clinicians make faster and more accurate diagnoses.
But strides are also being made in other areas.
In cardiovascular care, companies are using AI to analyze electrocardiograms to support the identification of heart arrhythmias and signs of heart failure. In neurology, AI can analyze EEG data to assess brain activity in patients with brain injuries. Other specialties, including ophthalmology and gastroenterology, are also exploring applications for AI. AEYE Health uses the technology to support diabetic retinopathy screening among ophthalmology patients. For patients receiving colonoscopies, OU Health uses AI to spot potentially cancerous colon polyps.
Moreover, companies outside the traditional medical device landscape are getting involved in this innovation. For instance, tech giant Apple has introduced features in its watches that monitor data for heart arrhythmia detection—a step towards integrating health monitoring into everyday life.
Generative and agentic AI introduce new possibilities for medical devices
The next wave of AI-enabled medical device innovation could look considerably different from the technologies that have dominated the market to date.
Many existing devices rely on AI models designed to perform specific tasks, such as detecting abnormalities in medical images, classifying physiological signals, or identifying patterns associated with particular conditions.
Generative and agentic AI could expand those capabilities.
Generative AI can produce new content, including text, images, and other forms of information, based on patterns learned from training data. In medical device applications, these capabilities could help transform complex diagnostic information into more accessible clinical insights, support interpretation of multiple data types, or create more interactive ways for clinicians to engage with medical technologies.
Agentic AI takes the concept further. Rather than simply generating a response to a request, an agentic system can pursue a defined objective by planning tasks, retrieving relevant information, interacting with other systems, and taking actions within established boundaries. For example, rather than independently analyzing a scan, an agentic AI system might coordinate multiple steps of a diagnostic workflow: retrieving relevant patient information, analyzing imaging results, comparing findings with previous examinations, and preparing information for clinician review.
Such capabilities could eventually help make medical devices more responsive, connected, and adaptable to clinical needs. However, these possibilities also raise new questions about how AI-enabled medical technologies should be developed, evaluated, and regulated.
Traditional AI-enabled devices frequently perform relatively narrow, predefined functions. Generative AI systems can produce variable outputs, while agentic systems may execute multiple interconnected actions to accomplish a goal.
This introduces additional considerations around accuracy, reliability, transparency, human oversight, and patient safety. For example, how should manufacturers validate a system capable of producing different responses to similar clinical questions? What safeguards are necessary when AI can initiate actions? And how can providers maintain appropriate control over increasingly autonomous technologies?
While no definitive answer has been made yet, the FDA released a discussion paper on generative AI-enabled medical devices in August 2026. The paper calls for input on risk assessment, premarket evaluation, and postmarket monitoring, and acknowledges the unique risks these devices could introduce.
Although the discussion paper does not establish new regulatory requirements, it reflects the increasingly complex questions associated with these technologies.
And while generative and agentic AI hold substantial promise, their clinical use remains an emerging area. A 2026 scoping review published in Nature identified promising applications of agentic AI across specialties including radiology, oncology, and emergency medicine, but found that most available research was exploratory and lacked robust clinical validation.
The future of AI in medical devices
For medical device manufacturers, this emerging landscape presents both opportunities and responsibilities. New AI capabilities could open doors to applications that were previously difficult or impossible to develop. But realizing their potential will require rigorous evidence, appropriate safeguards, and a clear understanding of how these technologies fit into clinical practice.
For more insights on how AI is transforming the industry, discover these six ways AI is being used in healthcare today. Or read our eBook on launching your medical device for best practices and guidance on how AI is impacting medtech.
Ready to find your next growth opportunity in medtech? Definitive Healthcare provides the data and intelligence you need to understand healthcare markets, identify high-potential opportunities, and connect with the organizations and decision makers who matter most to your commercial strategy. Book a demo today and uncover the patients, physicians, and care centers that need your medical device.
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