The new article addresses the aspects related to the quality management system in the context of medical devices based on innovative technologies.
Table of Contents
The Saudi Food & Drug Authority (SFDA), a country’s regulatory agency in the field of healthcare products, has published a guidance document dedicated to medical devices based on Artificial Intelligence (AI) and Machine Learning (ML) technologies. The document provides an overview of the applicable regulatory requirements, as well as additional clarifications to be taken into consideration by medical device manufacturers in order to ensure compliance thereto. At the same time, provisions of the guidance are non-binding in their legal nature, nor are intended to introduce new rules or impose new obligations. The authority also reserves the right to make changes to the guidance, should such changes be reasonably necessary to reflect corresponding amendments to the underlying regulations.
The scope of the guidance covers, inter alia, the aspects related to the Quality Management Systems (QMS) to be implemented by medical device manufacturers in order to ensure the proper quality of the products.
QMS: Key Points
In accordance with the applicable regulatory requirements, medical devices based on innovative technologies should be designed and manufactured in accordance with the requirements established by the Medical Devices Quality Management System (ISO 13485) providing that all the processes should be duly documented, and all the necessary actions should be taken to reduce mistakes and ensure the proper quality of medical devices. Furthermore, it is explicitly stated that the Quality Management System implemented by the manufacturer should comply with the requirements set forth by the existing legislation in the sphere of medical devices.
According to the QMS and Regulatory requirements, the organization, which designs and deploys the AI/ML, is responsible for implementing the QMS, which includes developing a quality policy, qualifying objectives, procedures, and project-specific plans that are customer focused. The authority mentions that it is also required to provide the appropriate level of resources (including people, tools, environment, etc.), needed for ensuring the effectiveness of the AI/ML lifecycle processes and activities in meeting SFDA regulation and customer requirements.
Main Elements
The document further describes each of the above-mentioned components in detail.
- Human Resources: the staff dealing with projects related to innovative technologies should have the necessary qualification and knowledge. In particular, they should have knowledge in the sphere of technology and software engineering.
- Infrastructure: all necessary equipment should be available through the product lifecycle processes. This includes the infrastructure necessary to develop and manufacture medical devices.
- Traceability: the QMS shall assist the organization to produce a systematic documentation of the AI/ML and its supporting design and development, including a robust and documented configuration and change management process, and identifying its constituent parts, to provide a history of changes made to it, and to enable recovery/recreation of past versions of the software, i.e., traceability of the AI/ML.
- Measurement and Monitoring: medical device manufacturers are obliged to develop and implement effective procedures for post-market surveillance. In particular, all the data related to the actual performance of medical devices should be properly collected and analyzed, paying special attention to complaints and issues identified. With respect to the latter, problem causes should be identified, and necessary actions should be duly taken to address them.
Improvement: Aspects for Consideration
The document also highlights the key aspects to be considered in the context of the improvement of AI/ML processes and products, namely:
- Evaluation of the AI/ML and its lifecycle processes should be based on defined responsibilities and predetermined activities including using leading and lagging safety indicators and collecting and analyzing appropriate quality data.
- Should it appear the product does not meet the respective requirements, or the processes are not duly followed, appropriate corrective actions should be taken.
- The products that do not meet the applicable specifications should be stored separately in order to prevent unintentional mix-up and supply, while all the non-conformities identified should be subject to rigorous analysis and assessment, and necessary actions should be performed to address them and underlying causes in order to ensure they will not reoccur in the future.
- Furthermore, any actions taken with respect to non-conformities identified should be subject to verification/validation before the device will be made available for marketing and use.
- The results of the analysis should be used to improve the safety, effectiveness, and performance of medical devices. The parties responsible for medical devices should duly develop and implement the processes for reporting adverse events to the authority. This includes the collection and analysis of information related to adverse events.
- Once the AI/ML device has been placed on the market, it is important to maintain vigilance for vulnerability to intentional and unintentional security threats as part of post-market surveillance.
In summary, the present SFDA guidance provides additional clarifications regarding the applicable regulatory requirements in the sphere of Quality Management Systems. The document highlights the key points to be taken into consideration in order to ensure compliance with the existing legislation, as well as the applicable standards.
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