The traceability principle states that medical AI algorithms should be developed together with mechanisms for documenting and monitoring the whole development lifecycle as well as the functioning of the AI tools in their environment. This will enable to increase transparency by providing detailed and complete information such as on the datasets used to train and evaluate the algorithms, the associated protocols and parameters, the variables included in the AI models, as well as any potential biases and limitations. Furthermore, continuous monitoring of the AI tools after their deployment in real-world practice is of paramount importance to evaluate performance and limitations over time, and hence to identify potential sources of error or drift from the training settings or previous states (e.g. due to a change in image quality or characteristics). For Traceability in AI for medicine and healthcare, we propose the following recommendations: