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Exploring the Endless Possibilities of AI and ML in Healthcare

A shift to an international approach to safety and performance?  

Regulators across the globe know, only to well, the challenges posed by software as a medical devices and specifically artificial intelligence (AI) and machine learning (ML). This is an area of great potential and fast pace innovation.  

AI and ML open up a new world of possibilities to improve health care, the options are, in theory, ‘endless’. However, with such a fast pace sector there are challenges in ensuring safety and performance. Regulations, guiding principles and standards must evolve.  

Three key voices – FDA, Health Canada and the MHRA have worked in collaboration to develop guiding principles in Good Machine Learning Practice for medical devices.  


Multi-Disciplinary Expertise Is Leveraged Throughout the Total Product Life Cycle

In-depth understanding of a model's intended integration into clinical workflow, and the desired benefits and associated patient risks, can help ensure that MLenabled medical devices are safe and effective and address clinically meaningful needs over the lifecycle of the device

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. 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 Population

Data 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 generalized to the population of interest. This is important to manage any bias, promote appropriate and generalizable performance across the intended patient population, assess usability, and identify circumstances where the model may underperform.

Training Data Sets Are Independent of Test Sets

Training 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 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. If available, accepted reference datasets in model development and testing that promote and demonstrate model robustness and generalizability across the intended patient population are used

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. 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-Al 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-Al team, rather than just the performance of the model in isolation

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. Considerations include the intended patient population, important subgroups, clinical environment and use by the Human-Al team, measurement inputs, and potential confounding factors

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. 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 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-Al team

This are areas where there can be advancements in GMLP, through work of the International Medical Device Regulators Forum (IMDRF), international standards organizations, and other collaborative bodies and regulators.  

The team behind the document hope these principles will drive the sector to: 

  1. Adopt good practices that have been proven in other sectors 
  1. Tailor practices from other sectors so they are applicable to medical technology and the health care sector 
  1. Create new practices specific for medical technology and the health care sector 

International partnerships offer strength and opportunity in improving safety and performance in this space. There is no ‘quick answer’ but industry and patients will benefit from clear and consistent approaches to these products. 

This document offers a starting point to how supporting the development, can allow for these products to offer safe and effective solutions that will improve, strengthen and reimagine healthcare solutions.