Action plan
The US Food and Drug Administration (FDA) released an action plan covering AI- and ML-based software that’s used as a medical device in January 2021. Almost two years in the making, it followed stakeholder feedback from an April 2019 discussion paper. Together with Health Canada and the UK Medicines and Healthcare products Regulatory Agency (MHRA), the FDA has also pinpointed ten “guiding principles” to inform the development of ML use. The International Coalition of Medicines Regulatory Authorities, meanwhile, has put forth recommendations to harmonise AI/ML regulations across the devices landscape.
But discussions about regulation cover not only technical and safety issues, but also data protection, liability and trust. Sara Gerke, assistant professor of law at Penn State Dickinson Law, specialises in this area, and she hasn’t been surprised by the time and energy expended on finding a balance between ensuring patient safety and providing developers with the flexibility to evolve their technology. “Solving the regulatory issues raised by medical AI is complex,” Gerke says. “In particular, it will be a challenge to develop a framework for adaptive AI/ML. If the AI/ML can continuously learn from new data, it will be essential to have an ongoing monitoring system in place to ensure the device remains safe, effective and free from bias.”
Pat Baird, head of global software standards at Philips, agrees that the very nature of the beast complicates regulations, adding that the technology itself “puts new stress” on existing ways of working. “We already have processes in place for change control, but since AI can learn and improve itself quickly, having a streamlined change process will help us improve product performance and help more patients at a faster pace than before,” he says. “This is not a challenge in deploying the technology, but rather a challenge in how to maximise the benefits of the technology.” Baird says the pace of adoption of AI/ML in devices has been hindered by the pandemic. “Things have been slower in the past few years than I originally anticipated, but this is mainly due to the strain that Covid has placed on the healthcare ecosystem,” he says. “Some of the time that people could have used working on AI, was spent managing Covid.” Even so, he warns against any move to fast-track the technology. “I don’t think that we want to rush the adoption – we need to take a thoughtful approach, identify the gaps, and work together to try to solve those issues,” he says. Gerke agrees. “Of course, regulation should not stifle innovation, [but] it is a delicate balancing act. It is important these devices are safe and effective, and that regulation adequately protects patients from harm,” she says, explaining it’s a simple fact that the
Medical Device Developments /
www.nsmedicaldevices.com Ten guiding principles from UK, US and Canada regulators:
1. The regulators recommend using multidisciplinary expertise throughout the total product life cycle of an AI/ML device. This entails understanding of how an AI/ML model should be integrated into clinical workflow, as well as of benefits and risks to users and patients over the full course of the device’s life cycle.
2. Implementation of good software engineering and security practices should be ensured, covering risk and data management as well as design processes robust enough to support integrity and authenticity of data.
3. Manufacturers and sponsors should ensure that clinical study participants as well as data sets effectively represent intended patient populations. Data collection protocols should be designed and calibrated to capture relevant patient population characteristics.
4. AI/ML medical device manufacturers and clinical study sponsors must keep training data sets independent of test sites. In other words, training datasets should be kept separate from testing site datasets.
5. Developers should base selected reference datasets on best available methods. This effort helps ensure collection of clinically relevant data, and that any reference data limitations are understood.
6. Model design should reflect the AI/ML device’s intended use, and reflect available pertinent data. Performance goals for testing should be based on well-understood clinical benefits and risks.
7. Manufacturers’ human-AI teams warrant appropriate focus. Human factors and usability are crucial considerations alongside model performance, according to the principle.
8. Manufacturers should ensure that model testing accurately demonstrates device performance under clinically relevant conditions, including intended patient populations, human-AI team interactions, measurement inputs and potential confounding factors.
9. Clear and essential information should be provided to users. AI/ML device developers and manufacturers should provide relevant, clear and accessible information to intended users such as patients or healthcare providers. Such data includes instructions for use; performance of the AI/ML model for appropriate subgroups as well as model training and testing data characteristics. Processes to provide device updates based on real-world performance monitoring should also be in place.
10. Performance monitoring of deployed models should be carried out in order to uphold or improve safety and performance, accompanied by periodic or continuous model training for more effective risk management.
Source:
www.emergobyul.com
law often lags behind technology. “It will be important to update the regulatory framework now and at regular intervals if needed. Even if the law is behind, it usually catches up and needs to be revised again because of new developments.”
If the AI/ML can continuously learn from new data, it will be essential to have an ongoing monitoring system in place to ensure the device remains safe, effective and free from bias.”
Sara Gerke Quality is crucial
On the surface, the fastest way to accommodate AI would be to absorb it into existing regulations that govern devices. “I believe AI fits into the current regulatory framework for medical devices,” says Gerke. “But within this framework, we need specific requirements for AI-based devices, and regulators need to develop standards specifically for AI.”
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