AI is no longer an experimental technology in healthcare; it's already embedded in diagnostic tools, patient monitoring systems, and clinical decision-making software across hospitals worldwide. But as these systems become more common, the conversation has fundamentally shifted. The question is no longer whether AI can transform medicine, but how to deploy it safely, govern it effectively, and integrate it into healthcare systems with confidence.

How Many AI Medical Devices Are Actually in Use Today?

The numbers tell the story of rapid adoption. The Food and Drug Administration (FDA) has authorized more than 1,400 AI and machine learning-enabled devices, up from around 950 in 2024. Europe and the United Kingdom are following a similar trajectory as manufacturers bring more AI-enabled technologies to market. Many of these devices now play a routine role in everyday healthcare, helping clinicians analyze medical images, monitor patients remotely, and identify patterns in data that would be difficult to detect manually at scale.

These technologies are moving beyond the lab and into clinical workflows that directly affect patient care. Some are now integrated into diagnosis, triage, and clinical decision-making processes. That proximity to patient outcomes has changed what regulators, healthcare providers, and patients expect from these systems.

What's Changed About How Companies Develop AI Medical Devices?

For years, medical device companies treated AI as a feature to add after the core product was built. Regulation was something to handle at the end of development, before seeking market approval. That approach no longer works. Today, governance, quality assurance, and regulatory compliance are being built into product development from the outset.

This shift reflects a more complex regulatory landscape. In Europe, the EU AI Act introduces new requirements for high-risk AI systems, including many medical technology applications. Manufacturers must also comply with existing frameworks such as the Medical Device Regulation and In Vitro Diagnostic Regulation. The United Kingdom 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.

"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," explained Mahesh Wale, UK and Ireland Business Unit Head for Retail, Consumer Goods, Travel, Hospitality, and Life Science at Cognizant.

Mahesh Wale, UK and Ireland Business Unit Head at Cognizant

This requires quality teams, regulatory specialists, and technology leaders to work more closely together from the beginning. It also demands reliable data that can be accessed throughout the product lifecycle.

Steps to Building Effective AI Governance in Medical Device Development

• Establish 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. Organizations must ensure those systems work together and provide reliable, well-structured information that can be accessed throughout the product lifecycle.
• Integrate Regulatory Requirements Early: Rather than addressing compliance at the end of development, embed regulatory expectations around risk management, data quality, transparency, and human oversight into the design process from the start.
• Plan for Post-Market Surveillance: As reporting requirements increase and more data becomes available, organizations need AI systems that can analyze larger datasets, identify emerging patterns, and prioritize issues that require investigation, allowing teams to focus on safety concerns that matter most.

Why Is Data Quality So Critical for AI Medical Devices?

AI systems rely on reliable and well-structured information. When data is fragmented or inconsistent, organizations struggle to meet regulatory expectations, move beyond isolated AI deployments, and scale technologies across the business. Regulators require traceability, quality teams need clear audit trails, and clinicians need confidence in the information they receive.

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 organizations analyze these larger datasets, identify emerging patterns, and prioritize issues that require further investigation.

Expectations around post-market surveillance are changing too. Organizations are under growing pressure to identify potential issues earlier and respond before they become wider patient safety concerns. Success depends not only on having access to more information, but on being able to turn it into meaningful insight quickly and with confidence.

How Does Trust Shape AI Adoption in Healthcare?

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. Organizations 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.

For organizations navigating this landscape, the message is clear: the companies 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 medical technology, making AI work will depend as much on those foundations as the technology itself.