In a significant step towards enhancing patient safety and trust in AI-powered healthcare technologies, the U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA) have introduced new guidelines to promote transparency in machine learning-enabled medical devices (MLMDs). Building on their 2021 collaboration, which established 10 guiding principles for good machine learning practice (GMLP), these regulatory bodies are now focusing on ensuring clear communication and transparency in the development and use of these advanced technologies.
In 2021, the FDA, Health Canada, and MHRA collectively identified GMLP principles designed to support the development of safe, effective, and high-quality AI and machine learning technologies within the medical device sector. These principles emphasized the importance of AI systems learning from real-world use and improving device performance over time. The newly introduced guidelines aim to ensure transparency for all stakeholders, including healthcare professionals, patients, caregivers, and regulatory bodies, thereby fostering trust and ensuring the safe and effective use of these advanced technologies.
The new guidelines for transparency build on two key GMLP principles: Principle 7, which focuses on the performance of the human-AI team, and Principle 9, which ensures users are provided with clear, essential information. Transparency in MLMDs involves communicating information about the device’s intended use, development, performance, and underlying logic to relevant audiences. This transparency is crucial for building trust and ensuring the safe and effective use of AI technologies in healthcare.
Transparency is critical for all parties involved in a patient’s healthcare journey. This includes healthcare professionals, patients, caregivers, administrators, payors, and governing bodies who make decisions about the use of these devices. Transparency supports patient-centered care, enhances the safety and effectiveness of devices, and facilitates informed decision-making. By clearly communicating the intended use and performance of MLMDs, stakeholders can better understand the risks and benefits, detect errors, and ensure equitable use of the technology.
Sharing appropriate information includes details about the device’s medical purpose, function, intended users, environments, target populations, performance, benefits, risks, and how the device fits into the healthcare workflow. It also involves explaining the device’s logic, bias management strategies, and ongoing performance monitoring. Information should be accessible through the user interface, including training materials, physical controls, display elements, packaging, labeling, and alarms. The software user interface should be optimized to provide personalized, adaptive, and reciprocal information.
Timely communication is essential throughout the product life cycle. This includes providing detailed information when acquiring or implementing a device, notifying users of updates or modifications, and offering targeted information during specific workflow stages. Human-centered design principles should be applied to support transparency. This approach addresses the whole user experience and involves relevant parties throughout the design and development process. It ensures that information is accessible, understandable, and usable by the intended audience.
The concept of human-centered design is integral to achieving effective transparency. This iterative process involves addressing the entire user experience and incorporating feedback from relevant parties throughout the design and development of MLMDs. By doing so, developers can ensure that users have all the necessary information to use the devices safely and effectively.
Troy Tazbaz, Director of the FDA’s Digital Health Center of Excellence, emphasized the importance of global collaboration in AI healthcare. “The responsible development of AI in healthcare is a central focus for regulators both in the U.S. and around the globe,” said Tazbaz. “This information holds the potential to influence the trust of healthcare professionals and patients toward a medical device and inform decisions regarding its use.”
The updated guiding principles for transparency in MLMDs mark a critical step in promoting safe and effective AI use in healthcare. By emphasizing the need for clear communication and human-centered design, the FDA, Health Canada, and MHRA are working to build trust and ensure that AI technologies benefit all stakeholders in the healthcare system. As AI continues to evolve, these principles will serve as a crucial framework for guiding the development, deployment, and regulation of AI-enabled medical devices.
This joint effort represents the third international collaboration on guiding principles for AI-enabled devices between the FDA, Health Canada, and the MHRA. Each of these documents demonstrates the FDA’s commitment to a global perspective on AI in healthcare and health equity. The comprehensive integration of these guiding principles across the entirety of the product life cycle ensures that informational requirements are adequately addressed, promoting the safe and effective utilization of MLMDs.
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Silver Spring, MD, USA 11/10/2020: Exterior view of the headquarters of US Food and Drug Administration (FDA). This federal agency approves medications, vaccines and food additives for human use. — Photo by grandbrothers on depositphotos
