FDA Release New Guidance for Modifications to AI/ML-Enabled Devices
Traditional pre-market review processes for medical devices were never designed to assess adaptive technologies such as AI/ML. In recent years the FDA has been under scrutiny from providers and medical researchers who believe that current regulatory approaches for AI/ML devices are inadequate. In their efforts to adapt regulatory processes, the FDA has published new draft guidance for AI/ML-enabled medical device modification to better protect and promote public health.
This new approach is designed to support minor, continuous improvements in ML device software functions (DSF). It also looks to ensure the effectiveness and safety of such technologies in line with previous FDA regulatory frameworks, dating from 2019 and 2021.
The guidance, entitled “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions”, provides recommendations related to Predetermined Change Control Plan (PCCP) in marketing submissions for ML-DSF devices.
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Under the new guidance, PCCPs would include detailed explanations of the planned ML-DSF modifications and the methodology used to implement and validate these changes. The FDA would also require assessments of any impact proposed modifications may have. This new approach will be applied to all AI/ML-enabled device software functions, including those which are part of medical devices or control hardware.
Medical devices that would be subject to the new guidance will include those which flag patient deterioration, forecast sepsis, identify patients who may be addicted to opioids, and predict heart failure hospitalisations, amongst others.
Brendan O’Leary, deputy director of the Digital Health Center of Excellence in the FDA’s Center for Devices and Radiological Health (CDRH), signalled that the new guidance is part of the CDHR’s commitment to improving medical and health equity. He said the proposed approach would ensure that “important performance considerations [...] are addressed in the ongoing development, validation, implementation, and monitoring of AI/ML-enabled devices.” The performance considerations O’Leary referenced include ethnicity, race, gender, disease severity, age, and geographical location.
In a press release published alongside the draft guidance, the FDA also indicated that under the new proposal, vendors would be required to describe how important information regarding modifications to ML-DSFs would be communicated to device users. This new requirement is another facet of the guidance geared towards advancing health equity in the US. Ultimately, the guidance should give healthcare providers and other users of AI/ML-enabled devices faster access to safe and effective advancements whilst also accelerating the rate of medical device innovations and helping drive precision medicine.
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