Abstract
Changes in mental or mood state are not only marked by changes in cognitive functioning—physiological and behavioral changes also accompany them. There is an ever-growing body of literature indicating that a combination of using mobile and wearable technology to collect physiological and behavioral markers followed by the use of artificial intelligence (AI) to analyze these data can provide objective markers for conditions such as depression and bipolar disorder (BD). In this regard, this chapter is intended to highlight the advantages of using AI-based technologies in clinical psychology settings. Readers are given a brief introduction into the fundamental process that enables machine learning (ML) technologies, a subbranch of AI concerned with pattern recognition, to make generalizable predictions. Also given, is an overview of data sources, in particular, information streams collectable using mobile technologies, typically used in AI-based mental health analysis. Finally, two in-depth case studies are presented which outline a range of different state-of-the-art AI and ML approaches for the detection of either depression or BD.
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