MHRA’s AI Medical Device Framework: What NHS Suppliers Need to Know About Cybersecurity and Compliance in 2026

Artificial intelligence is becoming deeply embedded in UK healthcare, from diagnostic imaging and clinical decision support to patient monitoring and workflow automation. In response, the Medicines and Healthcare products Regulatory Agency (MHRA) has confirmed it will publish a new regulatory framework for AI in medical devices in 2026, as set out in the government’s Life Sciences Sector Plan.

For NHS suppliers and healthtech companies, this is not just a regulatory milestone. It is a clear signal that AI assurance, cybersecurity, and lifecycle governance will be under increasing scrutiny. Organisations that treat AI safety and security as strategic priorities, rather than compliance afterthoughts, will be far better positioned for NHS adoption and long-term market access.

The Current Regulatory Landscape for AI Medical Devices in the UK

In the UK, AI-enabled software is regulated under the UK medical devices regulatory regime when it meets the definition of a medical device or an in vitro diagnostic (IVD).

MHRA’s guidance, “Software and artificial intelligence (AI) as a medical device”, explains how software, including AI and machine learning systems, should be assessed across the full product lifecycle. This includes:

• Intended medical purpose and device classification

• Clinical evaluation and performance validation

• Risk management and quality management systems

• Post-market surveillance and ongoing oversight

At present, AI is regulated within this broader framework rather than through AI-specific legislation. However, MHRA has openly recognised that AI introduces unique risks, particularly where systems are complex, opaque, or capable of change after deployment.

What Is Confirmed About the 2026 MHRA AI Framework

The Life Sciences Sector Plan confirms two important points for NHS suppliers:

• MHRA will publish a dedicated framework for AI in medical devices in 2026, following a review of existing regulation.

• An International Reliance Framework will be introduced by Autumn 2026, enabling greater reliance on approvals from trusted comparator regulators.

While the government has not yet published technical details, these commitments make it clear that AI will receive more tailored regulatory attention rather than being treated as “just another software update.”

Why Cybersecurity Will Be Central to AI Medical Device Regulation

Although the Sector Plan does not yet spell out cybersecurity requirements line-by-line, cybersecurity is inseparable from AI medical device safety.

AI systems introduce attack surfaces that go well beyond traditional software risks, including:

• Model manipulation or poisoning

• Data integrity and training data compromise

• Uncontrolled model updates or retraining

• Dependency on external data sources and APIs

From a regulatory perspective, these are not abstract IT risks — they are patient safety risks.

MHRA’s existing guidance already places strong emphasis on risk management across the device lifecycle. As AI-specific regulation matures, suppliers should expect cybersecurity assurance to be increasingly examined as part of:

• Safety and performance claims

• Post-market surveillance

• Incident reporting and corrective actions

For NHS buyers, cybersecurity weaknesses in AI systems can translate directly into operational disruption, data protection incidents, and clinical risk.

Likely Areas of Focus in the 2026 Framework

While the final framework has not yet been published, MHRA’s current signals suggest several areas where expectations are likely to increase.

Lifecycle Control for Adaptive AI

AI systems that change through retraining or updates present regulatory and security challenges. MHRA has already highlighted adaptivity as an area requiring careful oversight.

Suppliers should expect greater emphasis on:

• Controlled update processes

• Impact assessment of model changes

• Ongoing monitoring of real-world performance

From a cybersecurity perspective, uncontrolled change is a risk multiplier — particularly in clinical environments.

Transparency, Explainability, and Trust

MHRA has identified transparency and explainability as key issues for AI in healthcare. These are essential not only for clinical confidence but also for security assurance.

Opaque systems are harder to:

• Audit

• Test

• Monitor for anomalous behaviour

Clear documentation of how AI systems function, their limitations, and their failure modes will increasingly support both regulatory compliance and NHS trusts.

International Reliance and Shared Risk

The forthcoming International Reliance Framework is designed to streamline market access for products already approved elsewhere. However, reliance does not remove the need to demonstrate UK-appropriate cybersecurity controls, particularly where NHS infrastructure and patient data are involved.

Suppliers relying on overseas approvals should be prepared to show how global compliance aligns with UK expectations around security, resilience, and assurance.

What This Means for NHS Suppliers and Healthtech Companies

Even before the new framework is published, NHS suppliers should assume that AI assurance will become more demanding, not less.

Regulatory compliance, cybersecurity posture, and procurement readiness are increasingly intertwined. Suppliers that cannot clearly demonstrate secure-by-design development, robust risk management, and ongoing monitoring may face:

• Delays in regulatory approval

• Reduced confidence during NHS procurement

• Increased scrutiny following incidents or near-misses

In contrast, organisations that embed cybersecurity into AI governance early are better placed to support safe adoption and scale.

Practical Steps to Take Now

While detailed requirements will follow in 2026, there are sensible actions NHS suppliers can take today:

• Embed security-by-design in AI development
  Treat cybersecurity as a core safety control, not a compliance checkbox.

• Strengthen AI risk management and documentation
  Ensure risks related to data, models, and updates are clearly identified and managed.

• Prepare for continuous assurance
  AI medical devices should be monitored throughout their lifecycle, not just at release.

• Align technical, regulatory, and procurement teams
  NHS buyers increasingly expect consistency between security claims, regulatory submissions, and contractual commitments.

MHRA’s upcoming AI medical device framework represents a significant shift in how AI will be governed in UK healthcare. While the full details will not be known until 2026, the direction is clear: greater focus on lifecycle control, transparency, and risk with cybersecurity at the centre.

For NHS suppliers and healthtech companies, early preparation is both a compliance strategy and a competitive advantage. Those who invest now in secure, well-governed AI will be best placed to earn trust, protect patients, and succeed in an increasingly regulated healthcare market.