  Mental health conditions cause a great deal of distress or impairment;
depression alone will affect 11% of the world's population. The application of
Artificial Intelligence (AI) and big-data technologies to mental health has
great potential for personalizing treatment selection, prognosticating,
monitoring for relapse, detecting and helping to prevent mental health
conditions before they reach clinical-level symptomatology, and even delivering
some treatments. However, unlike similar applications in other fields of
medicine, there are several unique challenges in mental health applications
which currently pose barriers towards the implementation of these technologies.
Specifically, there are very few widely used or validated biomarkers in mental
health, leading to a heavy reliance on patient and clinician derived
questionnaire data as well as interpretation of new signals such as digital
phenotyping. In addition, diagnosis also lacks the same objective 'gold
standard' as in other conditions such as oncology, where clinicians and
researchers can often rely on pathological analysis for confirmation of
diagnosis. In this chapter we discuss the major opportunities, limitations and
techniques used for improving mental healthcare through AI and big-data. We
explore both the computational, clinical and ethical considerations and best
practices as well as lay out the major researcher directions for the near
future.
