Abstract
The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.