USA: FDA – An adaptive algorithm (e.g., a continuous learning algorithm) changes its behavior using a defined learning process. The algorithm adaptation or changes are implemented such that for a given set of inputs, the output may be different before and after the changes are implemented. These algorithm changes are typicallyimplemented and validated through a well-defined and possibly fully automated process that aims at improving performance based on analysis of new or additional data (Source: “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback”, April 2019).
The adaptation process can be intended to address several different clinical aspects, such as optimizing performance within a specific environment (e.g., based on the local patient population), optimizing performance based on how the device is being used (e.g., based on preferences of a specific physician), improving performance as more data are collected, and/or changing the intended use of the device. The adaptation process follows two stages: learning and updating. The algorithm “learns” how to change its behavior, for example, from the addition of new input types or adding new cases to an already existing training database. The “update” then occurs when the new version of the algorithm is deployed. As a result, given the same set of inputs at time A (before update) and time B (after update), the output of the algorithm may differ (Source: “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback”, April 2019).« Back to Glossary Index