Article 61(10) of the MDR establishes a pathway for the clinical evaluation of certain medical devices, where clinical data may not be the most appropriate method for demonstrating compliance with the General Safety and Performance Requirements (GSPR). It is especially applicable to products that do not provide a direct measurable clinical benefit or may not interact directly with the human body in a manner that warrants extensive clinical data.
Several key requirements must be met to demonstrate conformity under Article 61(10). Specifically, the results of the manufacturer’s risk management process should support the use of non-clinical testing methods instead of clinical trials. If there are high residual risks, clinical data may still be required. The specifics of the device’s interaction with the human body must be well understood and sufficiently characterized based on non-clinical sources. Devices with novel or uncertain interactions may still require clinical data to ensure safety and performance. Additionally, the claims made by the manufacturer must align with available data. If the device claims any direct clinical benefit, the use of Article 61(10) is no longer appropriate, and clinical data must be gathered. However, this does not imply that devices evaluated under Article 61(10) lack clinical benefits; most have an indirect clinical benefit derived from their technical performance. The fact that this indirect clinical benefit is not measurable through clinical data supports the justification for relying on Article 61(10).
However, the absence of clinical data does not automatically allow manufacturers to apply Article 61(10). Literature searches for clinical data can demonstrate that no clinical data are available and that it is unreasonable to expect trials to be conducted on this type of device. In other words, searches yielding no results can serve as supportive evidence for the Article 61(10) strategy.
A widely spread example of devices to which Article 61(10) may apply is software medical devices. Examples include diagnostic software that analyzes medical images or data, such as radiology software for processing X-ray images. The performance of such software can often be validated through bench testing and algorithm validation rather than clinical trials. Another example is Clinical Decision Support Systems (CDSS), which provide healthcare professionals with support for clinical decisions, such as drug interaction alerts based on patient data. These systems can be evaluated based on their technical performance and the accuracy of their algorithms.
If you are developing medical device software and struggling to generate clinical data for compliance, this pathway may be applicable to your device. Contact Starodub experts via the Contact page to discuss your specific case.