Substantial advancement in the diagnosis and treatment of psychiatric disorders may come from assembling diverse data streams from clinical notes, neuroimaging, genetics, and real-time digital footprints from smartphones and wearable devices. This is called “deep phenotyping” and often involves machine learning. We argue that incidental findings arising in deep phenotyping research have certain special, morally and legally salient features: They are specific, actionable, numerous, and probabilistic. We consider ethical and legal implications of these features and propose a practical ethics strategy for managing them.

In the last decade, mobile technology offered new opportunities and challenges in animal health surveillance. It began with the use of basic mobile phones and short message service (SMS) for disease reporting, and the development of smartphones and other mobile tools has expanded the possibilities for data collection. These tools assist in the collection of data as well as geo-referenced mapping of diseases, and mapping, visualization and identification of vectors such as ticks. In this article we share our findings about new technologies in the domain of animal health surveillance, based on several projects using a wide range of mobile tools, each with their specific applicability and limitations. For each of the tools used, a comprehensive overview is given about its applicability, limitations, technical requirements, cost and also the perception of the users.The evaluation of the tools clearly shows the importance of selecting the appropriate tool depending on the envisaged data to be collected. Accessibility, visualization and cost related to data collection differ significantly among the tools tested. This paper can thus be seen as a practical guide to the currently available tools.