CDRH Issues Guiding Principles for Transparency in Machine Learning-Enabled Medical Devices

On June 13, the FDA’s Center for Devices and Radiological Health (CDRH) issued guiding principles for enhancing transparency in machine learning-enabled medical devices.

The press release, which is attributed to Troy Tazbaz, the director of the Digital Health Center of Excellence in CDRH, touted the ability of health products powered by artificial intelligence and machine learning (AI/ML) to “learn from real-world use” and use that information “to improve the product’s performance.” He also cautioned that these devices pose “unique considerations due to their complexity and the iterative and data-driven nature of their development.”

According to Tazbaz, effective transparency in Machine Learning-enabled Medical Devices (MLMD) “ensures that information that could impact risks and patient outcomes is communicated to all the people who could be interacting with the device, including health care providers, patients, payors, and others, to help make informed decisions.” Thus, the FDA, Health Canada, and the U.K.’s Medicines and Healthcare products Regulatory Agency (MHRA) have jointly published a set of guiding principles to that effect.

The principles seek to establish effective transparency in order to ensure that the “information delivered to the intended user(s) or audience considers the device’s context of use, as well as the optimal mediums and strategies for successful communication. This information holds the potential to influence the trust of healthcare professionals and patients toward a medical device and inform decisions regarding its use.”