Artificial intelligence, machine learning, and mental healthcare: An introduction for mental health services and clinicians

The increasing rates, severity, and complexity of mental health problems are putting immense strain on Australia’s mental healthcare system. The rapidly advancing field of Machine Learning (ML) offers a promising pathway to more efficient and effective mental healthcare. Currently, however, there are multiple ethical and practical barriers to real-world implementation of ML-based tools. This document aims to introduce mental health clinicians to the opportunities and challenges involved with bringing ML into practice. We provide an overview of the ML process and how ML methods can overcome some of the limitations of traditional statistical methods. We then describe how ML-driven tools have the potential to improve detection, diagnosis, prognosis, and treatment of mental health problems, as well as automate clinical administration and enhance clinicians’ professional development. We include applied examples from the literature that, while not a comprehensive review, offer a glimpse into the diversity of ML-driven innovations in the mental health field. Finally, we outline the key challenges and risks involved with translating ML research into clinical practice, and the key next steps toward overcoming them.