Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support Effective ML System Design

High prevalence of mental illness and the need for effective mental healthcare, combined with recent advances in AI, has led to an increase in explorations of how machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. Despite great potential, and as an emerging research area, the development of effective ML applications bound up with many complex, interwoven challenges. Aiming to guide future research and identify new directions for this important domain, the paper presents an introduction to, and a systematic review of, current ML work in mental health from the computing and HCI literature. It surfaces common trends, gaps and challenges; provides concrete suggestions for a stronger integration of human-centered and multi-disciplinary approaches in research and development; and invites more consideration of the potentially far-reaching personal, social and ethical implications that ML interventions can have, if they are to find successful adoption in practice.