Integrating artificial intelligence into medical software in the UK has the potential to improve diagnosis, personalize treatments, and make healthcare processes more efficient. However, as AI technologies become more advanced, there is a growing need for regulation to ensure that these systems are both accurate and safe for patients.
Currently, medical AI in the UK is regulated under existing medical device regulations. Dedicated legislation specifically addressing medical AI is still being developed. In contrast, the European Union has already adopted its AI Act in 2024, which will be fully implemented by 2026. This difference means that developers in the UK must anticipate future requirements such as performance monitoring, post-market evaluation, clinical oversight, and model explainability.
James Belcher, Chief Operations Officer at Camgenium, commented on this evolving landscape: "For developers in the UK seeking regulatory certainty, abiding by international standards has become a reliable guide. Key international standards include ISO 13485 for quality management systems, IEC 62304 for medical device software life cycle processes, and ISO 14971 for risk management. Manufacturers who follow these international standards are more likely to avoid the costs of becoming compliant later in development."
Medical AI faces unique challenges compared to traditional medical device software. The main factor determining whether software qualifies as a medical device is if it directly or indirectly informs clinical care. All such software must be effective and safe to prevent harm to patients. However, issues like unexpected algorithm behavior, performance drift over time, and biases present new regulatory hurdles.
Belcher noted: "To ensure models are validated, manufacturers must conduct risk assessments and document risks identified and actions taken to ensure traceability. To avoid issues arising due to discrepancies between real-world data and training sets, great care should be taken in the early stages of engineering datasets."
Regulatory guidance is expected to shift towards increased real-world testing of AI models after they enter the market (post-market surveillance) and safeguards against changes in model performance over time (model drift). Manufacturers may also need to assess how their models perform across different clinical environments and practitioner groups with varying data quality.
Looking ahead at compliance challenges for innovators in this sector, Belcher said: "The greatest challenge for many medical AI innovators will be maintaining compliance within budget constraints and tight timelines rather than developing the technology itself. Early in the process there is pressure to focus on delivering a prototype and companies may decide to defer regulatory compliance. However once models are almost complete it becomes costly to put in place the evidence that regulators expect for earlier stages. This will result in delays and can create funding bottlenecks."
As regulation moves toward stricter requirements with higher assurance levels and greater transparency—while aligning globally—it could help build confidence among clinicians and patients about reliability and fairness of these systems internationally.
"For MedTech innovators," Belcher concluded,"the opportunity lies in embracing regulations as a mechanism for driving the future of the sector."
