10 Principles for Medical Devices Development

10 Principles for Medical Devices Development

Regulatory agencies across the globe, including the U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA), have come together to identify 10 guiding principles that can inform the development of Good Machine Learning Practice (GMLP). The goal of GMLP is to help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning (AI/ML).

The 10 guiding principles compiled by the regulatory agencies will hopefully lay the foundation for developing Good Machine Learning Practice that address the unique nature of medical devices using artificial intelligence and machine learning. They will also help cultivate future growth in this rapidly progressing field.

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The 10 guiding principles may also 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
  • Create new practices specific for medical technology and the health care sector

The 10 guiding principles are summarized below: 

1. Multi-Disciplinary Expertise Is Leveraged Throughout the Total Product Life Cycle: It’s important to have a thorough understanding of a device’s intended integration into clinical workflow (e.g. desired benefits, associated patient risks) to help ensure that the medical device is safe and effective and addresses clinically meaningful needs.

2. Good Software Engineering and Security Practices Are Implemented: Model design is implemented with attention to the “fundamentals”: good software engineering practices, data quality assurance, data management, and robust cybersecurity practices. 

 3. Clinical Study Participants and Data Sets Are Representative of the Intended Patient Population: Data collection protocols should ensure that the relevant characteristics of the intended patient population (e.g. 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 generalized to the population of interest. 

4. Training Data Sets Are Independent of Test Sets: Training and test datasets are selected and maintained to be appropriately independent of one another.

5. Selected Reference Datasets Are Based Upon Best Available Methods: Accepted, best available methods for developing a reference dataset (that is, a reference standard) ensure that clinically relevant and well characterized data are collected and the limitations of the reference are understood. 

6. Model Design Is Tailored to the Available Data and Reflects the Intended Use of the Device: Model 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. 

7. Focus Is Placed on the Performance of the Human-AI Team: Where 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.

8. Testing Demonstrates Device Performance During Clinically Relevant Conditions: Statistically sound test plans are developed and executed to generate clinically relevant device performance information independently of the training data set. 

9. Users Are Provided Clear, Essential Information: Users 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. 

10. Deployed Models Are Monitored for Performance and Re-training Risks Are Managed: Deployed 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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