For several years, AI has been viewed as one of the biggest opportunities for the medtech sector. With an industry survey finding that 91% of leaders were enthusiastic about its potential, early conversations centred on how it could transform healthcare delivery and improve patient outcomes.
Today, the conversation has moved on. AI is no longer something the industry is preparing for. It is already being used in diagnostic tools, patient monitoring systems and software that supports clinical decision-making.
For medtech companies, innovation and regulation now go hand in hand. Organisations can no longer focus solely on what AI can do, but also on how these systems can be deployed safely, governed effectively and integrated into healthcare systems with confidence and managed throughout their lifecycle.
AI is becoming part of everyday healthcare
AI-enabled medical devices are becoming increasingly common. In the United States, the Food and Drug Administration (FDA) has authorised more than 1,400 AI and machine learning-enabled devices, up from around 950 in 2024. Europe and the UK are following a similar path as manufacturers bring more AI-enabled technologies to market and into clinical practice.
Many of these technologies already play a role in everyday healthcare. They help clinicians analyse medical images, monitor patients remotely and identify patterns in data that would be difficult to detect at scale. Some are now being integrated into routine activities such as diagnosis, triage and clinical decision-making, placing them closer than ever to decisions that directly affect patient care.
As AI takes on a greater role in clinical settings, expectations are changing. Healthcare providers and regulators need confidence that these systems will perform consistently, that risks are understood and appropriate safeguards are in place. Questions around transparency, accountability and oversight become just as important as technical performance.
Regulation is becoming more demanding
As AI becomes more embedded in medical technology, manufacturers are navigating an increasingly complex regulatory environment. In Europe, the EU AI Act is introducing new requirements for high-risk AI systems, including many applications used within medical technology, while manufacturers must also continue to comply with existing frameworks such as the Medical Device Regulation and In Vitro Diagnostic Regulation.
The UK is taking a similar approach through initiatives including the Medicines and Healthcare products Regulatory Agency (MHRA)’s Software and AI as a Medical Device Change Programme and the AI Airlock regulatory sandbox. Together, these developments mean manufacturers are increasingly working across multiple regulatory frameworks, each with its own expectations around areas such as risk management, data quality, transparency and human oversight.
For many organisations, this is changing how compliance is approached. Rather than treating regulation as a final step before market approval, governance is increasingly being built into product development from the outset. That requires quality teams, regulatory specialists and technology leaders to work more closely together, supported by reliable data that can be accessed throughout the product lifecycle.
The data challenge behind AI adoption
Building AI into product development starts with strong data foundations. Information often sits across research and development, manufacturing, clinical, quality and post-market surveillance systems that were never designed to work together. As a result, organisations can struggle to build a complete and consistent view of their products throughout the lifecycle.
This becomes particularly important as organisations look to introduce AI into regulated processes. AI systems rely on reliable and well-structured information, while regulators require traceability, quality teams also need clear audit trails and clinicians need confidence in the information they receive. When data is fragmented or inconsistent, those expectations become much harder to meet. Organisations can also struggle to move beyond isolated AI deployments and scale these technologies across the business.
Many medtech companies have spent years investing in digital systems across different parts of the business. The next challenge is ensuring those systems work together. For many organisations, that will determine how successfully they can deploy AI while meeting growing regulatory expectations. For those that get these foundations right, regulation can become an enabler of faster and more confident AI adoption rather than a barrier to innovation.
How post-market surveillance is changing
The need for connected and reliable data becomes particularly clear in post-market surveillance. Traditionally, identifying safety signals has relied on manual processes, with teams reviewing incident reports, investigating adverse events and looking for patterns that may indicate emerging risks. As reporting requirements increase and more data becomes available, that approach is becoming harder to sustain.
The rollout of the European Database on Medical Devices (EUDAMED) is expected to create a more connected environment for monitoring medical device performance and safety across Europe. Greater visibility should strengthen patient protection and improve oversight, but it will also increase the volume of information manufacturers need to review. AI can help organisations analyse larger datasets, identify emerging patterns and prioritise issues that require further investigation, allowing regulatory and quality teams to focus on the issues that matter most.
Expectations around post-market surveillance are changing too. Organisations are under growing pressure to identify potential issues earlier and respond before they become wider patient safety concerns. In that environment, success depends not only on having access to more information, but on being able to turn it into meaningful insight quickly and with confidence.
Trust depends on more than compliance
Trust is one of the biggest factors shaping AI adoption in healthcare, and regulation provides an important foundation for building it. Healthcare providers need confidence that systems will perform reliably, patients need reassurance that technologies are being used responsibly and regulators need evidence that manufacturers understand the risks associated with their products and have effective controls in place.
That trust also depends on the resilience and transparency of the systems themselves. As connected medical devices become more common, cybersecurity risks can extend beyond data protection and, in some cases, affect the safe operation of devices. Organisations also need to understand how AI systems are trained, how outputs are generated and where human oversight should be applied.
These questions are becoming part of routine discussions between manufacturers, healthcare providers and regulators. Trust is built through the data, governance and oversight that sit behind AI, not through the technology alone.
Putting AI into practice
AI is already becoming part of everyday healthcare, especially within medical devices. The challenge now is ensuring these technologies can be deployed safely, consistently and at scale within increasingly complex regulatory environments.
The organisations that make the greatest progress will be those that build governance, quality and connected data into AI from the outset, rather than treating them as compliance requirements to address later. In medtech, making AI work will depend as much on those foundations as the technology itself.
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