UK regulators issue 10 ‘Guiding Principles’ for AI/ML medtech – DIGIT.FYI

  • Lauren
  • October 28, 2021
  • Comments Off on UK regulators issue 10 ‘Guiding Principles’ for AI/ML medtech – DIGIT.FYI

The UK, along with Canadian and US regulators, have identified and co-published 10 guiding principles that should be addressed when medical devices use artificial intelligence or machine learning software.
They cover key elements of GMLP – for example, having an in-depth understanding of a model’s intended integration into clinical workflow, and the desired benefits and associated patient risks, as well as selecting and maintaining training and datasets to be appropriately independent of each other.
Regulators envision that the guiding principles may be used to adopt good practices that have been proven in other sectors, tailor practices from other sectors so they are applicable to medical technology and the health care sector and create new practices specific for medical technology and the health care sector.

The guiding principles further identify areas where the International Medical Device Regulators Forum (IMDRF), international standards organisations and other collaborative bodies could work together to advance GMLP.
Areas of collaboration include research, creating educational tools and resources, regulatory policies and regulatory guidelines, international harmonisation, and consensus standards.


Medtech AI/ML 10 guiding principles in full
Multi-disciplinary expertise is leveraged throughout the total product life cycleIn-depth understanding of a model’s intended integration into clinical workflow, and the desired benefits and associated patient risks, can help ensure that ML-enabled medical devices are safe and effective and address clinically meaningful needs over the lifecycle of the device.
Good software engineering and security practices are implementedModel design is implemented with attention to the “fundamentals”. I.e., good software engineering practices, data quality assurance, data management, and robust cybersecurity practices.
These practices include methodical risk management and design process that can appropriately capture and communicate design, implementation, and risk management decisions and rationale, as well as ensure data authenticity and integrity.
Clinical study participants and data sets are representative of the intended patient populationData collection protocols should ensure that the relevant characteristics of the intended patient population (for example, in terms of age, gender, sex, race, and ethnicity), use, and measurement inputs are sufficiently represented in a sample of adequate size in the clinical study and training and test datasets, so that results can be reasonably generalised to the population of interest.
This is important to manage any bias, promote appropriate and generalisable performance across the intended patient population, assess usability, and identify circumstances where the model may underperform.
Training data sets are independent of test setsTraining and test datasets are selected and maintained to be appropriately independent of one another. All potential sources of dependence, including patient, data acquisition, and site factors, are considered and addressed to assure independence.
Selected reference datasets are based upon best available methodsAccepted, best available methods for developing a reference dataset (that is, a reference standard) ensure that clinically relevant and well characterised data are collected and the limitations of the reference are understood. If available, accepted reference datasets in model development and testing that promote and demonstrate model robustness and generalisability across the intended patient population are used.
Model design is tailored to the available data and reflects the intended use of the deviceModel design is suited to the available data and supports the active mitigation of known risks, like overfitting, performance degradation, and security risks.
The clinical benefits and risks related to the product are well understood, used to derive clinically meaningful performance goals for testing, and support that the product can safely and effectively achieve its intended use.
Considerations include the impact of both global and local performance and uncertainty/variability in the device inputs, outputs, intended patient populations, and clinical use conditions.
Focus is placed on the performance of the human-AI teamWhere the model has a “human in the loop,” human factors considerations and the human interpretability of the model outputs are addressed with emphasis on the performance of the Human-AI team, rather than just the performance of the model in isolation.
Testing demonstrates device performance during clinically relevant conditionsStatistically sound test plans are developed and executed to generate clinically relevant device performance information independently of the training data set. Considerations include the intended patient population, important subgroups, clinical environment and use by the Human-AI team, measurement inputs, and potential confounding factors.
Users are provided clear, essential informationUsers are provided ready access to clear, contextually relevant information that is appropriate for the intended audience (such as health care providers or patients) including: the product’s intended use and indications for use, performance of the model for appropriate subgroups, characteristics of the data used to train and test the model, acceptable inputs, known limitations, user interface interpretation, and clinical workflow integration of the model. Users are also made aware of device modifications and updates from real-world performance monitoring, the basis for decision-making when available, and a means to communicate product concerns to the developer.
Deployed models are monitored for performance and re-training risks are managedDeployed models have the capability to be monitored in “real world” use with a focus on maintained or improved safety and performance. Additionally, when models are periodically or continually trained after deployment, there are appropriate controls in place to manage risks of overfitting, unintended bias, or degradation of the model (for example, dataset drift) that may impact the safety and performance of the model as it is used by the Human-AI team.

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