It has the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Medical device manufacturers are using these technologies to innovate their products to better assist healthcare providers and improve patient care.
The concept of AI itself, however, is ambiguous if not controversial. Whatever promise it holds, AI, like any new healthcare technology, can present challenges to existing methods for ensuring safety and performance.
The safety and effectiveness of medical devices entering the market today are governed by regulations and private-sector consensus standards. These controls (standards and regulations) were developed alongside current technologies and are based on an extensive, shared understanding of how and how well they work.
With an emergent technology like AI, real-world experience is limited, which can hinder our ability to fully assess its effectiveness. Similarly, a lack of real-world experience with AI limits our understanding of its associated risks. AI-related risks are harder to quantify and mitigate; there may be unforeseeable and unpredictable hazards arising from the unique nature or function of AI.
As further advancements are made with AI technology, regulators may consider multiple approaches for addressing the safety and effectiveness of AI in healthcare, including how international standards and other best practices are currently used to support the regulation of medical software, along with differences and gaps that will need to be addressed for AI solutions. A key aspect will be the need to generate real-world clinical evidence for AI throughout its life cycle and the potential for additional clinical evidence to support adaptive systems.
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Good AI development processes and practices will be critical for ensuring the safety and performance of AI solutions in healthcare.
Whilst a lot of standards related to AI are still in development, there are many best practices that medical device manufacturers and healthcare organizations can adopt to help them ensure the safety and performance of AI in their processes:
Recognized by regulatory authorities in the US, Canada, Europe, and more, this international standard helps medical device manufacturers establish, document, and maintain a systematic risk management process across all phases of the lifecycle of a medical device. It can be used by all parts of the medical device industry, and by organizations dealing with the design, development, production, installation, or servicing of medical equipment, devices, and technology and helps to streamline the regulatory processes that enable entry to selected markets.
The standard’s requirements apply to all phases of the life cycle of a medical device. The process described applies to risks associated with a medical device, such as those related to biocompatibility, data and systems security, electricity, moving parts, radiation, and usability. It can also be applied to products that are not necessarily medical devices in some jurisdictions and can also be used by others involved in the medical device lifecycle.
There is still a need for the development of specific standards on risk management for AI as medical technology. Whereas the management processes and core activities for risk analysis, risk evaluation, risk control, and evaluation of overall residual risk will remain the same, data-driven AI systems will introduce new failure modes and hazards. Such guidance should cover different failure modes and hazards that are unique to AI systems. The guidance should identify specific considerations that AI developers should examine when applying the requirements of BS EN ISO 14971 to an algorithm.
To read more on the topic of risk management for medical devices, click here.
BS EN 62304:2006+A1:2015 standard applies to the development and maintenance of medical device software when the software is itself a medical device or when software is embedded or an integral part of the final medical device. The amendment adds requirements for legacy software which will help manufacturers demonstrate compliance with the relevant European Directives.
BS EN 62304:2006+A1:2015 is relevant to manufacturers of medical electrical equipment, medical devices incorporating software, and software that is itself a medical device.
Health software products are intended by their manufacturer for managing, maintaining or improving the health of individual persons, or the delivery of care. Some health software can contribute to a hazardous situation and risk control is therefore needed to prevent harm or reduce the likelihood of harm occurring. Testing of the finished product is not, by itself, adequate to address the safety of health software. Therefore, requirements for the processes by which the health software is developed are necessary.
BS EN 82304-1:2017 applies to the safety and security of health software products designed to operate on general computing platforms and intended to be placed on the market without dedicated hardware. Its primary focus is on the requirements for manufacturers. It covers the entire lifecycle including design, development, validation, installation, maintenance, and disposal of health software products.
When adapting to new technologies, such as AI, being able to collaborate and share knowledge with other organizations will be crucial to overcoming the safety and performance challenges of these products/services.
BS ISO 44001:2017 helps organizations to build and develop effective competitive business relationships based upon a collaborative approach. It can be used to manage relationships on several different levels whether your needs focus on a single application between operating divisions or more complex relationships like consortia and joint ventures.
As AI technology continues to advance rapidly in the medical devices sector, accessing the standards your business needs to adapt to these innovations does not have to be complicated and time-consuming. Our tailored BSI Knowledge subscription service provides flexibility, access, visibility, and control over the standards and insights your team needs to adopt emerging processes. Request to learn more.
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