FAQ's

These describe ‘what’ changes the provider/manufacturer anticipates as the algorithm learning occurs. These changes could be to algorithm performance, inputs, or intended use of the SaMD in scope.

Once the scope and quantum of anticipated changes are identified, the ACP specifies the steps to be followed to ensure patient sfety and device effectiveness. These include monitoring and even controlling for mitigating any significant performance loss of the SaMD and subsequent risk to the patient’s health.

These practices span across the SaMD solution components mentioned above, from data management to inference. They are intended to ensure the development and oversight of cutting-edge technologies used in these SaMDs – particularly with many ML methods being very opaque in terms of their inner workings. The FDA is working with other regulatory organizations, industry bodies, and standards organizations to define these practices for the manufacturers of AI/ML-based SaMDs.

There needs to be a clearly labeled indication that the SaMD employs AI/ML technologies that evolve and the end-use of its output. Patient engagement over these issues is imperative to drive adoption for the AI/ML based SaMDs.

Healthcare data has numerous demographic parameters that impact the overall delivery of health services. There is a possibility that training using this data unintentionally introduces these biases into the algorithms for AI/ ML based SaMDs. SaMD providers need to incorporate methods that eliminate or neutralize such bias from the solution specifications. Numerous industry and academic groups are working on enhancing these methods for SaMD applications.